Particle analysis and sorting apparatus and methods

ABSTRACT

Described herein are apparatuses for analyzing an optical signal decay. In some embodiments, an apparatus includes: a source of a beam of pulsed optical energy; a sample holder configured to expose a sample to the beam; a detector comprising a number of spectral detection channels configured to convert the optical signals into respective electrical signals; and a signal processing module configured to perform a method. In some embodiments, the method includes: receiving the electrical signals from the detector; mathematically combining individual decay curves in the electrical signals into a decay supercurve, the supercurve comprising a number of components, each component having a time constant and a relative contribution to the supercurve; and numerically fitting a model to the supercurve.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/593,995, entitled “Particle Analysis and SortingApparatus and Methods, filed Dec. 3, 2017, and is a continuation-in-partof U.S. Nonprovisional patent application Ser. No. 15/599,834, entitled“Particle Analysis and Sorting Apparatus and Methods,” filed May 19,2017 and issuing as U.S. Pat. No. 9,952,133 on Apr. 24, 2018; which is acontinuation of U.S. Nonprovisional patent application Ser. No.14/879,079, entitled “Particle Analysis and Sorting Apparatus andMethods,” filed Oct. 8, 2015 and issued as U.S. Pat. No. 9,658,148 onMay 23, 2017; which claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/062,133, filed on Oct. 9, 2014, the contents ofeach of which are herein incorporated by reference in their entirety.

GOVERNMENT SUPPORT CLAUSE

This invention was made with government support under grant number1R43GM12390601 awarded by the National Institutes of Health. Thegovernment has certain rights in the invention.

INTRODUCTION

This disclosure pertains to the fields of Particle Analysis, ParticleSorting, and Multiplexed Assays. In particular, embodiments disclosedherein are capable of increased multiplexing in Flow Cytometry, CellSorting, and Bead-Based Multi-Analyte Assays.

BACKGROUND

Cellular analysis and sorting have reached a high level ofsophistication, enabling their widespread use in life science researchand medical diagnostics alike. Yet for all their remarkable success astechnologies, much remains to be done in order to meet significant needsin terms of applications.

One area of continuing unmet need is that of multiplexing. Multiplexingrefers to the practice of labeling cells, beads, or other particles withmultiple types of biochemical or biophysical “tags” simultaneously anddetecting those tags uniquely, so as to generate a richer set ofinformation with each analysis. The most commonly used tags inmicroscopy and flow cytometry are fluorescent molecules, orfluorophores. A fluorophore may be a naturally occurring fluorophore; itmay be an added reagent; it may be a fluorescent protein [like, e.g.,Green Fluorescence Protein (GFP)] expressed by genetic manipulation; itmay be a byproduct of chemical or biochemical reactions, etc.Fluorophores may be used as they are, relying on their native affinityfor certain subcellular structures such as, e.g., DNA or RNA; or theymay be linked to the highly specific biochemical entities known asantibodies, in a process referred to as conjugation. As a particularantibody binds to a matching antigen, often on the surface of a cell,the fluorophore conjugated to that antibody becomes a “tag” for thatcell. The presence or absence of the fluorophore (and therefore of theantigen the fluorophore-conjugated antibody is intended to specificallybind to) can then be established by excitation of the cells in thesample by optical means and the detection (if present) of thefluorescence emission from the fluorophore. Fluorescence emission into acertain range of the optical spectrum, or band, is sometimes referred toin the art as a “color;” the ability to perform multiplexed analysis istherefore sometimes ranked by the number of simultaneous colorsavailable for detection.

The use of multiple distinct tags (and detection of their associatedcolors) simultaneously allows the characterization of each cell to amuch greater degree of detail than possible with the use of a singletag. In immunology particularly, cells are classified based on theirexpression of surface antigens. The identification of a large number ofdifferent surface antigens on various types of cells has motivated thecreation of a rich taxonomy of cell types. To uniquely identify theexact type of cell under analysis, it is therefore often necessary toperform cell analysis protocols involving simultaneously a large numberof distinct antibody-conjugated tags, each specifically designed toidentify the presence of a particular type of antigen on the cellsurface.

In flow cytometry of the prior art, methods have been devised tosimultaneously label cells with up to twenty or more differentfluorescent tags and detect their respective colors. Commerciallyavailable instrumentation is generally limited to simultaneous detectionof fifteen colors or less, and most commonly less than about ten colors.One of the main challenges of routinely performing highly multiplexedanalysis (as the practice of simultaneously detecting more than about adozen separate colors is sometimes called) is the technical difficultyof keeping detection of each color (and its associated tag) separatefrom detection of all the other ones. FIG. 1 illustrates one key aspectof the challenge of multiplexed measurement of fluorescence in the priorart. The graph in this FIG. 1 depicts various fluorescence emissioncurves (thin solid lines) of intensity (I) as a function of wavelength(λ), all curves having been normalized to their respective peakintensities. In applications of fluorescence detection, it is verycommonly desirable to employ several different colors, or spectralbands, of the electromagnetic spectrum, and to assign each band to adifferent fluorophore. Different fluorophores can be selected, on thebasis of their average emission spectra, so as to obtain relativelydense coverage of a certain range of the electromagnetic spectrum, andthereby maximize the amount of information that can be extracted in thecourse of a single experiment or analysis “run.” However, when strivingto maximize spectral coverage, one of the common undesirableconsequences is spectral overlap. The shaded portions in FIG. 1illustrate the problem caused by spectral overlap between adjacentfluorescence spectra. In this particular illustrative example, fivespectral fluorescence “bands” or colors (the five emission curvespeaking at different wavelengths) span a certain desired range of theelectromagnetic spectrum, such as, e.g., the visible portion of thespectrum from about 400 nm to about 750 nm in wavelength. The shadedportions indicate sections of the spectrum where it is impossible, usingspectral means alone, to decide whether the signal comes from one or theother of the two bands adjacent to the overlapping region; accordingly,the portions of the spectrum corresponding to significant overlap arecommonly discarded, resulting in inefficient use of the spectrum.Additionally, even after discarding such portions, residual overlapremains in the other portions, resulting in contamination of one bandfrom signals from other bands. Attempts at negating the deleteriouseffects of such contamination go under the heading of “compensation.”This spectral overlap problem is variously described in the literatureand the community as the “crosstalk,” the “spillover,” the “compensationproblem,” etc., and it is a major factor in limiting the maximum numberof concurrent spectral bands, or colors, that can be employed in afluorescence detection experiment.

It would be desirable, then, to provide a way to perform highlymultiplexed analyses of particles or cells with a reduced or eliminatedimpact of spectral crosstalk.

In cell analysis of the prior art, methods have also been devised tosimultaneously label cells with up to thirty or more different tags anddetect their respective characteristics. For example, the techniqueknown in the art as mass cytometry employs not fluorescence as a way todistinguish different tags, but mass spectrometry, where the tagsincorporate not fluorophores, but different isotopes of rare earthsidentifiable by their mass spectra. One major drawback of this approachis that the protocol of analysis is destructive to the sample, the cellsand their tags becoming elementally vaporized in the process ofgenerating the mass spectra. This approach is therefore not suited tothe selection and sorting of cells or other particles following theiridentification by analysis.

It would be further desirable, then, to provide a way to performselection and sorting of particles or cells based on nondestructivehighly multiplexed analysis with a reduced or eliminated impact ofspectral crosstalk.

In bead-based multiplexing assays of the prior art, the substrate forthe capture of analytes is the surface of a color-coded microsphere(also referred to as “bead”). The measurement of analytes (e.g.,antigens) by so-called sandwich immunoassays is typically performedwith, e.g., antigen-specific primary antibodies attached to the surfaceof the microsphere; the analytes are captured by the primary antibodies;and the reporting is typically performed using, e.g., secondaryantibodies conjugated to fluorescent reporter molecules. Similar methodsof the prior art are used for measurement of other analytes, includingproteins, enzymes, hormones, drugs, nucleic acids, and other biologicaland synthetic molecules. To provide for simultaneous measurement ofdifferent analytes (multiplexing), each microsphere is internallystained with one or more dyes (colors) in precise amounts spanning arange of discrete levels. Each particular level of dye A (and optionallyin combination with particular levels of dye B, dye C, etc.) is assignedto, e.g., a specific primary capture antibody attached to the surface ofthe microsphere. As color-coded beads are mixed with a sample, they eachcapture a certain analyte; a second step provides the secondary bindingof the reporter molecule. The resulting bead+analytes+reporters complexis then passed through a particle analysis apparatus substantially verysimilar to a flow cytometer, where one light source is used to excitethe dye or dyes in each bead, and another light source is used to excitethe reporter fluorophore. The unique color code (combination of specificstaining levels of dye A and optionally dye B, dye C, etc.) assigned toeach capture entity allows the simultaneous analysis of tens or hundredsof analytes in a sample; the dye-based color coding of each bead is usedto classify the results as the beads pass through. In current commercialofferings, there is a practical limit to the number of color-coded beadtypes that can be used simultaneously in a multiplex assay. Onefluorescence detection spectral band is reserved for the reportermolecules, reducing the spectral range available for coding the beads;accordingly, it has been challenging to fashion more than two or threeseparate fluorescence detection bands out of the remaining availablespectrum. Each band providing about 10 discrete levels of fluorescencefor multiplexing, the total number of possible combinations is about 10for one dye, about 100 for two dyes, and about 1,000 for three dyes.Current commercial offerings cap at 500 the number of practicallyavailable multiplexing combinations, limiting the number of individualanalytes that can be examined in a single measurement run.

It would be further desirable, then, to provide a way to performbead-based multiplexing with a greater number of simultaneouslydistinguishable beads, to enable the performance of multiplexing assayswith a greater number of simultaneously measured analytes.

SUMMARY

One aspect of the present disclosure is directed to an apparatus foranalyzing an optical signal decay. In some embodiments, the apparatusincludes: a source of a beam of pulsed optical energy; a sample holderconfigured to expose a sample to the beam; a detector including a numberof spectral detection channels, the channels being sensitive to distinctwavelength sections of the electromagnetic spectrum and being configuredto detect optical signals resulting from interactions between the beamand the sample, the channels being further configured to convert theoptical signals into respective electrical signals; a first optical pathfrom the source of the beam to the sample; a second optical path fromthe sample to the detector; and a signal processing module configured toperform a method. In some embodiments, the method includes: receivingthe electrical signals from the detector; mathematically combiningindividual decay curves in the electrical signals into a decaysupercurve, the supercurve comprising a number of components, eachcomponent having a time constant and a relative contribution to thesupercurve; and numerically fitting a model to the supercurve.

In some embodiments, numerically fitting comprises using at least oneof: linear regression, nonlinear regression, least squares fitting,nonlinear least squares fitting, partial least squares fitting, weightedfitting, constrained fitting, Levenberg-Marquardt algorithm, Bayesiananalysis, principal component analysis, cluster analysis, support vectormachines, neural networks, machine learning, deep learning, andcombinations thereof.

In some embodiments, each time constant and each relative contributioneach correspond to at least one adjustable parameter of the model,wherein the model comprises a plurality of parameters, and wherein oneor more of the plurality of parameters is adjusted by fitting.

In some embodiments, the source of the beam of pulsed optical energy isan internally modulated laser.

In some embodiments, the signal processing module comprises one of: anFPGA, a DSP chip, an ASIC, a CPU, a microprocessor, a microcontroller, asingle-board computer, a standalone computer, and a cloud-basedprocessor.

In some embodiments, the optical signals comprise a fluorescence signal.

In some embodiments, the sample comprises a suspension of particles; theapparatus further including: a flow path for the suspension ofparticles; and a flowcell configured as an optical excitation chamberfor generating the optical signals from interactions between the beam ofpulsed optical energy and the particles, such that the flowcell isconnected with the flow path, the first optical path, and the secondoptical path.

In some embodiments, the apparatus comprises a flow cytometer.

In some embodiments, the apparatus further includes: a particle sortingactuator connected with the flow path; an actuator driver connected withthe actuator, the driver configured to receive actuation signals fromthe signal processing module; and at least one particle collectionreceptacle connected with the flow path.

In some embodiments, the particle sorting actuator is based on at leastone flow diversion in the flow path.

In some embodiments, the particle sorting actuator is based on one of: atransient bubble, a pressurizable chamber, apressurizable/depressurizable chamber pair, and a pressure transducer.

Another aspect of the present disclosure is directed to an apparatus foranalyzing an optical signal decay. In some embodiments, the apparatusincludes: a source of a beam of pulsed optical energy; a sample holderconfigured to expose a sample to the beam; a detector including a numberof spectral detection channels, the channels being sensitive to distinctwavelength sections of the electromagnetic spectrum and being configuredto detect optical signals resulting from interactions between the beamand the sample, the channels being further configured to convert theoptical signals into respective electrical signals; a first optical pathfrom the source of the beam to the sample; a second optical path fromthe sample to the detector; and a signal processing module configured toperform a method. In some embodiments, the method includes: receivingthe electrical signals from the detector; mathematically combiningindividual decay curves in the electrical signals into a decaysupercurve, the supercurve comprising a number of components, eachcomponent having a time constant and a relative contribution to thesupercurve; allocating individual components of the supercurve todiscrete bins of predetermined time constants; and numerically fitting amodel to the supercurve.

In some embodiments, numerically fitting comprises using at least oneof: linear regression, nonlinear regression, least squares fitting,nonlinear least squares fitting, partial least squares fitting, weightedfitting, constrained fitting, Levenberg-Marquardt algorithm, Bayesiananalysis, principal component analysis, cluster analysis, support vectormachines, neural networks, machine learning, deep learning, andcombinations thereof.

In some embodiments, each time constant and each relative contributioneach correspond to at least one adjustable parameter of the model,wherein the model comprises a plurality of parameters, and wherein oneor more of the plurality of parameters is adjusted by fitting.

In some embodiments, the source of the beam of pulsed optical energy isan internally modulated laser.

In some embodiments, the signal processing module comprises one of: anFPGA, a DSP chip, an ASIC, a CPU, a microprocessor, a microcontroller, asingle-board computer, a standalone computer, and a cloud-basedprocessor.

In some embodiments, the optical signals comprise a fluorescence signal.

In some embodiments, the sample includes a suspension of particles; theapparatus further including: a flow path for the suspension ofparticles; and a flowcell configured as an optical excitation chamberfor generating the optical signals from interactions between the beam ofpulsed optical energy and the particles, such that the flowcell isconnected with the flow path, the first optical path, and the secondoptical path.

In some embodiments, the apparatus comprises a flow cytometer.

In some embodiments, the apparatus further includes: a particle sortingactuator connected with the flow path; an actuator driver connected withthe actuator, the driver configured to receive actuation signals fromthe signal processing module; and at least one particle collectionreceptacle connected with the flow path.

In some embodiments, the particle sorting actuator is based on at leastone flow diversion in the flow path.

In some embodiments, the particle sorting actuator is based on one of atransient bubble, a pressurizable chamber, apressurizable/depressurizable chamber pair, and a pressure transducer.

An apparatus for analyzing an optical signal decay, comprising:

-   -   a source of a beam of pulsed optical energy;    -   a sample holder configured to expose a sample to said beam;    -   a detector, the detector comprising a number of spectral        detection channels, said channels being sensitive to distinct        wavelength sections of the electromagnetic spectrum and being        configured to detect optical signals resulting from interactions        between said beam and said sample, said channels being further        configured to convert said optical signals into respective        electrical signals;    -   a first optical path from said source of said beam to said        sample;    -   a second optical path from said sample to said detector; and    -   a signal processing module, capable of:    -   receiving said electrical signals from said detector;    -   mathematically combining individual decay curves in said        electrical signals into a decay supercurve, said supercurve        comprising a number of components, each component having a time        constant and a relative contribution to said supercurve;    -   extracting time constants from said supercurve; and    -   quantifying the relative contribution of individual components        to said supercurve.

An apparatus for analyzing an optical signal decay, comprising:

-   -   a source of a beam of pulsed optical energy, wherein said source        of said beam of pulsed optical energy is an internally modulated        laser;    -   a flowcell configured as an optical excitation chamber for        exposing to said beam a sample comprising a suspension of        particles and for generating optical signals from interactions        between said beam and said particles;    -   a detector, the detector comprising a number of spectral        detection channels, said channels being sensitive to distinct        wavelength sections of the electromagnetic spectrum and being        configured to detect said optical signals, said channels being        further configured to convert said optical signals into        respective electrical signals, wherein said optical signals        comprise a fluorescence signal;    -   a first optical path from said source of said beam to said        sample, said first optical path being connected with said        flowcell;    -   a second optical path from said sample to said detector, said        second optical path being connected with said flowcell;    -   a signal processing module, wherein said signal processing        module comprises one of an FPGA, a DSP chip, an ASIC, a CPU, a        microprocessor, a microcontroller, a single-board computer, a        standalone computer, and a cloud-based processor, said signal        processing module being further capable of:    -   receiving said electrical signals from said detector;    -   mathematically combining individual decay curves in said        electrical signals into a decay supercurve, said supercurve        comprising a number of components, each component having a time        constant and a relative contribution to said supercurve;    -   extracting time constants from said supercurve; and    -   quantifying the relative contribution of individual components        to said supercurve;    -   a flow cytometer;    -   a flow path for said suspension of particles, said flow path        being connected with said flowcell;    -   a particle sorting actuator connected with said flow path,        wherein said particle sorting actuator is based on at least one        flow diversion in said flow path, and wherein said particle        sorting actuator is further based on one of a transient bubble,        a pressurizable chamber, a pressurizable/depressurizable chamber        pair, and a pressure transducer;    -   an actuator driver connected with said actuator, said driver        being configured to receive actuation signals from said signal        processing module; and    -   at least one particle collection receptacle connected with said        flow path.

An apparatus for analyzing an optical signal decay, comprising:

-   -   a source of a beam of pulsed optical energy;    -   a sample holder configured to expose a sample to said beam;    -   a detector, the detector comprising a number of spectral        detection channels, said channels being sensitive to distinct        wavelength sections of the electromagnetic spectrum and being        configured to detect optical signals resulting from interactions        between said beam and said sample, said channels being further        configured to convert said optical signals into respective        electrical signals;    -   a first optical path from said source of said beam to said        sample;    -   a second optical path from said sample to said detector; and    -   a signal processing module, capable of:    -   receiving said electrical signals from said detector;    -   mathematically combining individual decay curves in said        electrical signals into a decay supercurve, said supercurve        comprising a number of components, each component having a time        constant and a relative contribution to said supercurve;    -   allocating individual components of said supercurve to discrete        bins of predetermined time constants; and    -   quantifying the relative contribution of individual components        to said supercurve.

An apparatus for analyzing an optical signal decay, comprising:

-   -   a source of a beam of pulsed optical energy, wherein said source        of said beam of pulsed optical energy is an internally modulated        laser;    -   a flowcell configured as an optical excitation chamber for        exposing to said beam a sample comprising a suspension of        particles and for generating optical signals from interactions        between said beam and said particles;    -   a detector, the detector comprising a number of spectral        detection channels, said channels being sensitive to distinct        wavelength sections of the electromagnetic spectrum and being        configured to detect said optical signals, said channels being        further configured to convert said optical signals into        respective electrical signals, wherein said optical signals        comprise a fluorescence signal;    -   a first optical path from said source of said beam to said        sample, said first optical path being connected with said        flowcell;    -   a second optical path from said sample to said detector, said        second optical path being connected with said flowcell;    -   a signal processing module, wherein said signal processing        module comprises one of an FPGA, a DSP chip, an ASIC, a CPU, a        microprocessor, a microcontroller, a single-board computer, a        standalone computer, and a cloud-based processor, said signal        processing module being further capable of:    -   receiving said electrical signals from said detector;    -   mathematically combining individual decay curves in said        electrical signals into a decay supercurve, said supercurve        comprising a number of components, each component having a time        constant and a relative contribution to said supercurve;    -   allocating individual components of said supercurve to discrete        bins of predetermined time constants; and    -   quantifying the relative contribution of individual components        to said supercurve;    -   a flow cytometer;    -   a flow path for said suspension of particles, said flow path        being connected with said flowcell;    -   a particle sorting actuator connected with said flow path,        wherein said particle sorting actuator is based on at least one        flow diversion in said flow path, and wherein said particle        sorting actuator is further based on one of a transient bubble,        a pressurizable chamber, a pressurizable/depressurizable chamber        pair, and a pressure transducer;    -   an actuator driver connected with said actuator, said driver        being configured to receive actuation signals from said signal        processing module; and    -   at least one particle collection receptacle connected with said        flow path.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a wavelength diagram illustrating the spectral overlap(spillover) in multiplexing approaches of the prior art.

FIG. 2 is a time-domain diagram illustrating a frequency-based approachto measuring single-exponential fluorescence lifetime of the prior art.

FIG. 3 is a time-domain diagram illustrating a time-correlatedsingle-photon counting approach to measuring fluorescence lifetime ofthe prior art.

FIG. 4A is a linear-linear time-domain diagram.

FIG. 4B is a log-linear time-domain diagram illustrating asingle-exponential decay curve resulting from pulsed excitation.

FIG. 4C is a linear-linear time-domain diagram.

FIG. 4D is a log-linear time-domain diagram illustrating adouble-exponential decay curve resulting from pulsed excitation.

FIG. 5 is a wavelength-lifetime diagram coupled with a wavelengthdiagram illustrating a multiplexing approach dense in both spectralbands and lifetime bins in accordance with one embodiment.

FIG. 6 is a wavelength-lifetime diagram coupled with a wavelengthdiagram illustrating a multiplexing approach sparse in both spectralbands and lifetime bins in accordance with one embodiment.

FIG. 7 is a schematic illustration of a system configuration of anapparatus for analysis of single particles in a sample in accordancewith one embodiment.

FIG. 8 is a schematic illustration of a system configuration of anapparatus for analysis and sorting of single particles in a sample inaccordance with one embodiment.

FIG. 9 is a schematic representation of the light collection anddetection subsystem of a particle analyzer/sorter with a single spectraldetection band in accordance with one embodiment.

FIG. 10 is a schematic representation of the light collection anddetection subsystem of a particle analyzer/sorter with multiple spectraldetection bands in accordance with one embodiment.

FIG. 11A is a time-domain diagram illustrating a signal processingsequence in accordance with one embodiment: interaction envelope due toa flowing particle crossing the beam.

FIG. 11B is a time-domain diagram illustrating a signal processingsequence in accordance with one embodiment: excitation pulses.

FIG. 11C is a time-domain diagram illustrating a signal processingsequence in accordance with one embodiment: effective excitation pulses.

FIG. 11D is a time-domain diagram illustrating a signal processingsequence in accordance with one embodiment: fluorescence emission pulseswith decay curves.

FIG. 11E is a time-domain diagram illustrating a signal processingsequence in accordance with one embodiment: segmentation of individualpulse signals.

FIG. 11F is a time-domain diagram illustrating a signal processingsequence in accordance with one embodiment: construction of asupercurve.

FIG. 12A is a log-linear time-domain diagram illustrating atriple-exponential decay supercurve constructed from individual pulsesignals resulting from pulsed excitation.

FIG. 12B is a log-linear time-domain diagram illustrating the process ofcomputing successive index-pair differences, determining supercurve kneepoints, and determining supercurve time-constant branches.

FIG. 13A is a schematic plan-view illustration of one step, or state, ofa particle analysis/sorting method that uses a sorting actuator inaccordance with one embodiment.

FIG. 13B is a schematic plan-view illustration of one step, or state, ofa particle analysis/sorting method that uses a sorting actuator inaccordance with one embodiment.

FIG. 14A is a schematic cross-sectional illustration of one step, orstate, of a particle analysis/sorting method with two sorting states andone-sided actuation in accordance with one embodiment.

FIG. 14B is a schematic cross-sectional illustration of one step, orstate, of a particle analysis/sorting method with two sorting states andone-sided actuation in accordance with one embodiment.

FIG. 15A is a schematic cross-sectional illustration of one step, orstate, of a particle analysis/sorting method with two sorting states andone-sided actuation in accordance with one embodiment.

FIG. 15B is a schematic cross-sectional illustration of one step, orstates, of a particle analysis/sorting method with two sorting statesand one-sided actuation in accordance with one embodiment.

FIG. 16A is a schematic cross-sectional illustration of one step, orstate, of a particle analysis/sorting method with two sorting states andtwo-sided actuation in accordance with one embodiment.

FIG. 16B is a schematic cross-sectional illustration of one step, orstate, of a particle analysis/sorting method with two sorting states andtwo-sided actuation in accordance with one embodiment.

FIG. 17A is a schematic cross-sectional illustration of one state of aparticle analysis/sorting method with five sorting states and one-sidedactuation that uses multiple sorting channels in accordance with oneembodiment.

FIG. 17B is a schematic cross-sectional illustration of one state of aparticle analysis/sorting method with five sorting states and one-sidedactuation that uses multiple sorting channels in accordance with oneembodiment.

FIG. 17C is a schematic cross-sectional illustration of one state of aparticle analysis/sorting method with five sorting states and one-sidedactuation that uses multiple sorting channels in accordance with oneembodiment.

FIG. 17D is a schematic cross-sectional illustration of one state of aparticle analysis/sorting method with five sorting states and one-sidedactuation that uses multiple sorting channels in accordance with oneembodiment.

FIG. 18A is a flow chart describing a sequence of principal operationsinvolved in the performance of a method of lifetime analysis inaccordance with one embodiment.

FIG. 18B is a flow chart describing a sequence of principal operationsinvolved in the performance of a method of particle analysis inaccordance with one embodiment.

FIG. 18C is a flow chart describing a sequence of principal operationsinvolved in the performance of a method of highly multiplexed particleanalysis in accordance with one embodiment.

FIG. 19 is a schematic representation of a data processing system toprovide an analyzer/sorter in accordance with one embodiment.

FIG. 20A is a time-domain diagram illustrating computation of an areaunder the supercurve in accordance with one embodiment.

FIG. 20B is a time-domain diagram illustrating computation of an areaunder the supercurve in accordance with one embodiment.

FIG. 20C is a time-domain diagram illustrating computation of an areaunder the supercurve in accordance with one embodiment.

DETAILED DESCRIPTION

One possible solution to the spectral overlap problem in highlymultiplexed particle and cell analysis would be to utilize, besidespectral information, another type of information with which to index orencode the tags used to label cell characteristics. By adding anindependent quantity that can be detected and measured, one cansignificantly increase the number of combinations available to label andidentify cell types. There would follow then a reduced need to fit alarge number of independent spectral bands into a limited region of theelectromagnetic spectrum, since the total number of availablecombinations could be allocated based on two independent quantitiesinstead of just one.

One improvement disclosed herein is used to provide fluorescencelifetime as that independent quantity, to be combined with spectrallabeling to generate a highly multiplexed set of independentcombinations with which to uniquely tag different cell characteristicsor cell types with a reduced or eliminated impact of spectral crosstalk.

Another improvement disclosed herein is used to provide the combinationof fluorescence lifetime and spectral fluorescence labeling to aid notonly in the highly multiplexed analysis of cells or other particles, butalso in the selection and sorting of cells or other particles with areduced or eliminated impact of spectral crosstalk.

Yet another improvement disclosed herein is used to provide thecombination of fluorescence lifetime and spectral fluorescence labelingin bead-based multiplexing for antigen, protein, nucleic-acid, and othermolecular assays. By providing for lifetime multiplexing of the dyesused to color code the beads, the present disclosure greatly expands thenumber of possible combinations that can be used to identify individualbead types. By adding the capability of distinguishing beads based onfluorescence lifetime binning, this number can be increased to 10,000,100,000, or more, leading to orders-of-magnitude reductions in the costof running, e.g., highly multiplexed immunoassays, protein assays, ornucleic-acid assays.

Fluorescence lifetime is an aspect of the fluorescence emission processgoverned by quantum-mechanical laws. Fluorescence is the absorption byan atom or molecule of a packet of optical energy (a photon) of acertain wavelength and the subsequent emission by the same atom ormolecule of a packet of optical energy (another photon) at a longerwavelength. The amount of time elapsed between absorption and emissionvaries stochastically, but given an ensemble of isolated identical atomsor molecules, the frequency distribution of such elapsed times of theentire ensemble follows an exponential decay curve. The time constant ofsuch a curve (the 1/e time) is referred to as the lifetime for thatfluorescence transition for that atom or molecule.

Different molecular entities display different fluorescence transitions,characterized by different optimal wavelengths of optical absorption,different peak wavelengths of optical emission, and differentfluorescence lifetimes. Certain molecular entities display fluorescencetransitions with similar spectral characteristics (the profiles ofemission as a function of wavelength schematically illustrated inFIG. 1) but with different fluorescence lifetimes. And other molecularentities display fluorescence transitions with different spectralcharacteristics but with similar fluorescence lifetimes. Accordingly,molecular entities may be selected based on spectral characteristics(spectral emission profile) and fluorescence lifetime as essentiallyindependent quantities.

In order to use fluorescence lifetime as a multiplexing parameter inparticle analysis, one needs to provide the means to measure it. FIG. 2describes one aspect of measurements of fluorescence lifetime as carriedout in one approach in the prior art. The graph in this FIG. 2 depictstwo curves of optical intensity (I) as a function of time (t). In thisapproach, the intensity of optical excitation (thick solid line) ismodulated at a certain frequency, and the resulting fluorescence signal(thin dashed line) is analyzed. The effect of a finite fluorescencelifetime manifests itself primarily in the phase shift between themodulated excitation and the modulated emission curves. The maindrawback of this approach (so-called “phase-sensitive” or“frequency-domain” fluorescence lifetime) is that it can only probe onefluorescence lifetime component at a time, and is poorly suited toanalysis of samples where more than one lifetime component should bemeasured simultaneously. Certain improvements disclosed herein are usedto overcome this limitation.

FIG. 3 illustrates the principle behind another approach to measurementof fluorescence lifetime in the prior art. This approach has beenreferred to in the literature as Time-Correlated Single-Photon Counting(TCSPC), and has been used particularly in fluorescence lifetime imagingapplications (FLIM). The graph depicted in this FIG. 3 shows two curvesof intensity (I) as a function of time (t), both normalized to unit peakintensity, and a histogram with associated bins. The first curve (thicksolid line) represents any one of many identical excitation pulses usedto interrogate a portion of the sample; the second curve (thin dashedline) represents the inferred fluorescence emission response from theportion of the sample under interrogation. This second curve is notmeasured directly, but is instead inferred by a numerical fit to ahistogram. A typical hypothetical histogram is shown as a series ofboxes superimposed upon the second curve. This histogram represents thefrequency distribution of arrival times of single fluorescence emissionphotons following excitation by a pulse. By exciting the same portion ofthe sample many times, a histogram is collected that faithfully reflectsthe underlying fluorescence decay curve. The main drawback of thisapproach is the very principle it is based on: single-photon counting.The method only works if a single photon is, on average, emitted as aresult of excitation. For typical decay curves, it is not uncommon torequire between tens of thousands and millions of repeated excitationsin order to acquire enough statistics in the histogram for acceptableaccuracy of results. Even at high pulse repetition rates, this approachnecessarily results in dwell times (the time spent acquiring data on asingle portion of the sample) on the order of milliseconds to seconds.Accordingly, TCSPC is an approach that has been successfully applied tostationary samples, but which is poorly suited to samples that arerapidly varying, unstable, flowing, or generally needing to be analyzedrapidly. Certain improvements disclosed herein are used to overcome thislimitation.

FIGS. 4A-D illustrate the importance of direct time-domain measurementsof fluorescence lifetime. In each of FIGS. 4A-D, a graph depicts theevolution in time (t) of the intensity (I) of two curves: the opticalexcitation pulse (shown as thick solid lines 410, 430, 450, and 470, inthe four graphs, respectively) and the optical emission curve (shown asthin dashed lines 425, 445, 465, and 485, in the four graphs,respectively), both being normalized to unit peak intensity. In FIGS. 4Aand 4C, each of the two curves in each graph 420 and 460 are plotted ona linear-linear scale; in FIGS. 4B and 4D, each of the two curves ineach graph 440 and 480 are plotted on a log-linear scale (also known asa “semilog” scale). The graphs 420 and 440 in FIGS. 4A and 4B illustratethe same curves, just plotted on different scales; likewise, the graphs460 and 480 in FIGS. 4C and 4D illustrate the same curves, just plottedon different scales. In fluorescence processes, a molecule (which can benaturally occurring, such as certain dyes; or manmade, such as themajority of fluorophores in current use) exhibits a propensity forabsorption of optical energy within a certain range of wavelengths(referred to as its absorption spectrum), followed by emission ofoptical energy into a different range of wavelengths (referred to as itsemission spectrum). The process of absorption and emission influorescence is governed by quantum mechanics and is influenced byseveral factors. Some of those factors are intrinsic to the molecule;other factors are environmental factors. Emission of a fluorescentphoton occurs stochastically; given a large enough collection ofidentical molecules (an ensemble), the collective emission of theensemble will appear to decay over time. For a homogeneous ensemble[depicted in FIGS. 4A and 4B], the cumulative curve of fluorescenceemission can be represented by a single decay lifetime (shownschematically as τ_(a) 421 in this case). In the semilog plot 440 ofFIG. 4B, the single-decay nature of the dashed emission curve 445 isevidenced by the presence of a single straight slope (indicated by itscorresponding lifetime τ_(a) 441, corresponding to the same τ_(a) as 421in plot 420). For a heterogeneous ensemble [depicted in FIGS. 4C and 4D]consisting of two distinct populations, the cumulative curve offluorescence emission can be better represented by a compound functioncomprising two different decay lifetimes (shown schematically as τ_(b)461 and τ_(c) 462 in this case). In the semilog plot 480 of FIG. 4D, thedouble-decay nature of the dashed emission curve 485 is evidenced by thepresence of two straight-sloped branches (indicated by their respectivelifetimes τ_(b) 481 and τ_(c) 482, respectively corresponding to τ_(b)461 and τ_(c) 462 in plot 460) joined at a “knee.” The shape of thedecay curve gives information regarding the environment of the moleculesin the ensemble. Assuming that the ensemble in FIGS. 4C and 4D consistedof a single kind of molecular entity, the appearance of two distinctlifetimes would suggest that molecules in the ensemble are exposed totwo different environmental influences, one of them causing asignificant alteration to the native fluorescence lifetime of themolecules. Without a direct recording in real time of the actual shapeof the emission decay curve of the ensemble of fluorescence molecules,the distinction between the cases depicted in FIGS. 4A-B and FIGS. 4C-Dwould be lost, and with it the information regarding the environment ofthe molecular species. The analysis made possible by this directtime-domain approach can be variously referred to as multi-component ormulti-exponential fluorescence decay analysis.

A practical example of application of the principle of analysis ofmulti-exponential, or multi-component, fluorescence decay is found inthe analysis of cells. A eukaryotic cell consists primarily of amembrane, a cytoplasm, a nucleus, and various subcellular cytoplasmicstructures. The biochemical microenvironment experienced by a moleculewithin a cell is greatly affected by factors such as the localconcentration of electrolytes, local pH, local temperature, etc. When afluorophore enters a cell, its microenvironment may be very different,depending on whether the fluorophore is freely floating in thecytoplasm, binds to a molecule (e.g., RNA) or to an enzyme or othersubcellular structures within the cytoplasm, or crosses the nuclearmembrane to bind to, e.g., DNA in the nucleus. When exposed to opticalexcitation, the sub-ensemble of fluorophores bound to DNA in the nucleusmay exhibit a very different lifetime from, e.g., the sub-ensemble offluorophores freely floating in the cytoplasm. By analyzing the compounddecay curve of the entire ensemble, one must be able to distinguishbetween the two (or more) different contributions to the lifetime, as anaverage single lifetime will blur the desired information and present anincomplete and/or misleading picture of the situation.

If the cell to be analyzed is stationary (as, e.g., adhered to asubstrate, or grown on a substrate, suitable for placement under amicroscope), existing microscopy tools could be used to spatiallyresolve physical locations within the cell, perform, e.g., highlyrepetitious experiments on single pixels (or voxels) spanning very smallportions of the cell, and repeat these measurements over all pixels (orvoxels) comprising the cell. There are however, many instances when thatapproach is not desirable, and it would be instead advantageous for thecell to be analyzed to be moving swiftly past the point ofinterrogation. One instance is when it is desirable to complete a set ofmeasurements on a cell or on a group of cells in a very short time, asis generally the case in clinical diagnostic applications, wheretime-to-results is a critical parameter on which may depend patients'health or lives. A related instance, also of great relevance in clinicaldiagnostics and drug discovery and development, but increasingly alsorecognized as important in basic scientific research, is when it isdesirable to complete a certain set of measurements on a very largecollection of cells in a practical amount of time, so as to generatestatistically relevant results not skewed by the impact of individualoutliers. Another instance is when it is desirable to performmeasurements on a cell in an environment that mimics to the greatestdegree possible the environment of the cell in its native physiologicalstate: As an example, for cells naturally suspended in flowing liquids,such as all blood cells, adhesion to a substrate is a very unnaturalstate that grossly interferes with their native configuration. There areyet instances where the details of the physical location within the cell(details afforded, at a price of both time and money, by high-resolutionmicroscopy) are simply not important, but where a proxy for specificlocations within the cell would suffice, given prior knowledge (based onprior offline studies or results from the literature) about thecorrelation between specific cell locations and values of the proxymeasurement.

There are also instances where, regardless of the speed at which ameasurement is carried out, and therefore regardless of whether themeasurement is performed on a flow cytometer, under a microscope, orunder some other yet experimental conditions, it would be desirable toreduce or eliminate the spectral crossover problem. Many analyticalprotocols in cell biology research, drug discovery, immunology research,and clinical diagnostics are predicated on the concurrent use ofmultiple fluorophores, in order to elucidate various properties ofhighly heterogeneous samples consisting of diverse cell populations,sometimes with uncertain origin or lineage. The spectral crossoverinherent in such concurrent use of available fluorophores presentlylimits the multiplexing abilities of tools in current use—be they cellsorters, cell analyzers, image-based confocal scanning microscopes, orother platform. Various schemes have been developed to quantify andmitigate the deleterious impact of spectral crossover, and are generallyreferred to as compensation correction schemes. These schemes, however,suffer from overcomplexity, lack of reproducibility, and difficulty inthe proper training of operators. It would be therefore advantageous toprovide various analytical platforms with a way to multiplex complexmeasurements without the same attendant spectral crossover issue as iscurrently experienced.

There are yet instances of cellular analysis where it is desirable toperform fluorescence lifetime measurements on molecular species nativeto the systems under study. In this case the process of fluorescence issometime referred to as autofluorescence or endogenous fluorescence, andit does not depend on the introduction of external fluorophores, butrather relies on the intrinsic fluorescence of molecules already present(generally naturally so) in the cell to be analyzed. Endogenousfluorescence is similar to the fluorescence of externally introducedfluorophores, in being subject to similar effects, such as the influenceof the molecular microenvironment on fluorescence lifetime. Accordingly,it would be advantageous to be able to resolve different states ofendogenous fluorescence on an analytical platform, so as to provide forsimple and direct differentiation between cells belonging to differentpopulations known to correlate with different values of endogenousfluorescence lifetime of one or more natively present compounds. Oneexample of practical application of the principle of endogenousfluorescence is in the differential identification of cancer cells fromnormal cells based on metabolic information.

FIG. 5 schematically illustrates a principle of the present disclosure,namely the ability to improve the multiplexing capacity of an analyticalinstrument by using fluorescence lifetime information. At the bottom ofFIG. 5 is a graph 550 depicting various fluorescence emission curves561-565 (thin solid lines) of intensity (I) as a function of wavelength(λ), all curves having been normalized to their respective unit peakintensity. In contrast to the prior art, where distinction betweendifferent fluorophores as cell labels or “markers” is performedexclusively by spectral means (the horizontal wavelength axis λ in graph550), in the present disclosure a separate, orthogonal dimension ofanalysis is added: the vertical lifetime Taxis in the graph 500 at top.Each fluorescence emission curve is represented by a “band”, shown inthe figure as a shaded vertical strip 511-515: FL₁ (511), FL₂ (512), FL₃(513), FL₄ (514), FL₃ (515), . . . . Similarly, different fluorescencelifetime values are represented by different values of τ, groupedtogether in “bins”, shown in the figure as shaded horizontal strips521-525: τ₁ (521), τ₂ (522), τ₃ (523), τ₄ (524), τ₅ (525), . . . . Eachof the bins is intended to schematically represent a relatively similargroup of lifetimes: the variation among the various lifetime values inthe τ₁ bin will generally be smaller than the difference between theaverage lifetimes of the τ₁ and τ₂ bins, the variation among the variouslifetime values in the τ₂ bin will generally be smaller than thedifference between the average lifetimes of the τ₁ and τ₂ bins and willalso generally be smaller than the difference between the averagelifetimes of the τ₂ and τ₃ bins, and so on.

FIG. 5 makes it plain that the wavelength axis and its associated bandsnow represent only one dimension of a virtual plane. The second, addeddimension of this virtual plane is represented by the fluorescencelifetime axis τ and its associated bins in graph 500. The schematicintersections of the wavelength bands and the lifetime bins are shown ingraph 500 as darker shaded regions 530 (of which only a few are labeled)in the λ-τ plane. The increased multiplexing of the present disclosureis exemplified by the fact that, for every one of the spectral(wavelength) bands generally available to current analytical platforms,the present disclosure offers several possible multiplexed lifetimes: Asan example, the fluorescence band FL₂ (512) supports multiplefluorescence lifetime bins τ₁ (521), τ₂ (522), τ₃ (523), τ₄ (524), τ₅(525), . . . . For a system with n distinct fluorescence bands and mdistinct lifetime bins, the total theoretical number of independentcombinations is n×m; to use a practical example, for a system with 6distinct fluorescence bands and 4 distinct lifetime bins, there are6×4=24 mutually independent multiplexed combinations available.

FIG. 5 also describes how a specific example of a multiplexedcombination would be resolved in the present disclosure. From thewavelength axis a particular spectral band (say, FL₃ band 513) isselected for analysis. This particular spectral band in practice wouldbe selected by spectral optical means, such as one or more of thin-filmfilters, dichroic beam splitters, colored glass filters, diffractiongratings, and holographic gratings, or any other spectrally dispersingmeans suitable for the task and designed to pass this band ofwavelengths preferentially over all others. The resulting optical signalcould still comprise any of a number of fluorescence lifetimes,depending on the instrument design and on the nature of the sample. Thespectral optical signal filtered through as FL₃ is detected, convertedto electronic form, and sent to an electronic signal processing unit fordigitization and further elaboration; see FIG. 10. The signal processingunit (further described below in reference to FIGS. 7 and 19) performsan analysis of the decay characteristics of the optical signalscorresponding to the particle under study, and allocates the variouscontributions to the overall signal from each possible band of lifetimevalues. A virtual electronic “bin” corresponding to the specific bin τ₄(524) of lifetimes would receive a value corresponding to the fractionof the signal that could be ascribed as resulting from a lifetime decaywithin the acceptable range relevant for the τ₄ band. The combination ofthe spectral filtering for FL₃ performed optically on the emitted signaland the lifetime filtering for τ₄ performed digitally on theelectronically converted optical signal results in narrowing downanalysis to a single multiplexing element: the shaded intersection 540marked with a thick solid square in graph 500 of FIG. 5.

The specific choice of FL₃ and τ₄ is only illustrative, in the sensethat any of the intersections between detectable spectral fluorescencebands and resolvable lifetime bins are potentially simultaneouslyaccessible by analysis—resulting in the increased multiplexing abilitydescribed above as desirable. FIG. 5 shows explicitly a set of allowablemultiplexed intersections for a prophetic example comprising 5 distinctfluorescence bands and 5 distinct lifetime bins: a resulting set of upto 5×5=25 separate, mutually independent combinations. This example isillustrative only: the number of possible combinations is not limited to25, being given instead by the number of individually separablefluorescence spectral bands multiplied by the number of individuallyseparable lifetime values bins. The theoretical maximum number ofindividually separable lifetime bins is related to the samplingfrequency of digitization, the repetition rate of excitation pulses, andthe duration (width) of each excitation pulse. In the limit ofexcitation pulses much shorter than lifetimes of interest (which aretypically in the tens of picoseconds to tens of nanoseconds, and whichin some cases reach microsecond levels or greater), the maximum numberof separable lifetime bins is given by the pulse repetition perioddivided by twice the digitization sampling period, plus one. Electronic,optical, and other noise effects in actual systems may significantlyreduce this theoretical maximum. In the case where one of the lifetimesused is substantially longer than the interpulse period, which typicallymay be in the range of 5 to 500 ns, more preferably may be in the rangeof 10 to 200 ns, and most preferably may be in the range of 20 to 100ns, the decay may be so slow as to produce an effectively flat opticalemission baseline. For example, certain lanthanide complexes, including,without limitation, complexes of praseodymium, neodymium, samarium,europium, gadolinium, terbium, dysprosium, holmium, erbium, thulium, andytterbium, display long-lived luminescence with lifetimes ranging from0.03 μs to 1500 μs. Measurement of the effective baseline produced bythe long-lived fluorescent or luminescent compound, as compared to thebaseline in the absence of the compound, allows the determination of thepresence and, if present, of the amount of the long-lived compound. Along-lived compound can be used either in isolation or in addition toshorter-lived fluorescent species.

In practice it may be desirable to implement less than the theoreticalmaximum number of multiplexed combinations available. Some of thepractical reasons that may factor into the criteria for such a choice(which may be hard coded during design, or may alternately be left up tothe instrument operator) may include: the desire to reduce thecomputational complexity required for a full implementation of thepossible combinations; the desire to reduce the computational timerequired to perform a statistically acceptable analysis on the number ofpossible combinations; the desire to manufacture or to obtain a simpler,smaller, less costly instrument than would be needed for a fullimplementation of the theoretical maximum number of possiblecombinations; the desire for an operator to be able to operate theanalytical platform with a minimum of specialized training; and thedesire for a robust instrument designed to perform a reduced set ofoperations in a highly optimized fashion. Whichever the motivation, onemay choose to produce a “sparse” multiplexed configuration, where someof the possible multiplexing choices have been removed.

In one embodiment of the present disclosure, such sparseness isintroduced in the lifetime domain: Only a few of the possible lifetimebins are provided, the rest being removed and being replaced by gapsbetween the provided lifetime bins [e.g., removing bins τ₂ (522) and τ₄(524) in FIG. 5]. The advantage of this configuration over a denselypopulated lifetime configuration is that the relative sparseness of thelifetime bins simplifies the process of digitally distinguishing thelifetime contributions of the remaining bins to the optical emissionsignal.

In another embodiment of the present disclosure, the sparseness ofmultiplexing is introduced in the spectral domain: Only a few of thepossible wavelength bands are provided, the rest being removed and beingreplaced by gaps between the provided spectral bands [e.g., removingbands FL₂ (512) and FL₄ (514) in FIG. 5]. The advantage of thisconfiguration over a densely populated spectral configuration is thatthe relative sparseness of the spectral bands simplifies the handling ofany residual spectral overlap.

FIG. 6 shows an illustrative example (graph 600) of yet anotherembodiment of the present disclosure, where the sparseness ofmultiplexing has been introduced in both the spectral (graph 650 atbottom) and the lifetime domains simultaneously: Only a few of thepossible wavelength bands have been provided, the rest having beenremoved and being indicated in the figure by the gaps between theprovided spectral bands [e.g., between bands FL₁ (611) and FL₃ (613) andbetween bands FL₃ (613) and FL₅ (615)]; and only a few of the possiblelifetime bins have been provided, the rest having been removed and beingindicated in the figure by the gaps between the provided lifetime bins[e.g., between bins τ₁ (621) and τ₃ (623) and between bins τ₃ (623) andτ₅ (625)]. The resulting configuration, while having considerably fewerintersection points than the theoretical maximum, is however advantagedover a densely populated spectral and lifetime configuration by thereduction in the number of hardware components, the reduction in thecomplexity of the signal processing algorithms, by the relative increasein robustness and accuracy of the signal processing results, and by therelative simplicity of a training protocol for operation of theassociated instrument platform.

FIG. 7 illustrates schematically a system configuration of an exemplaryembodiment of the present disclosure, which provides an apparatus forhighly multiplexed particle analysis in a sample. In another embodiment,it provides an apparatus for lifetime analysis of particles in a sample.One or more light source 750, e.g., a laser, produces one or moreoptical energy (light) beams 722 with desired wavelength, power,dimensions, and cross-sectional characteristics. One or more modulationdrivers 752 provide modulation signal(s) 702 for the one or morerespective light sources, resulting in the beam(s) 722 becoming pulsed.The modulation drivers may optionally be internal to the lightsource(s). The pulsed beam(s) are directed to a set of relay optics 754(which can include, without limitation, lenses, mirrors, prisms, oroptical fibers), which may additionally optionally perform abeam-shaping function. Here relay optics will be intended to representmeans to transmit one or more beams from one point in the system toanother, and will also be intended to represent means to shape one ormore beams in terms of dimensions and convergence, divergence orcollimation. The output pulsed beam(s) 732 from the beam-shaping relayoptics are directed to another optional set of relay optics 758 (whichcan include, without limitation, lenses, mirrors, prisms, or opticalfibers), which may additionally optionally perform a focusing function.The beam-shaping optics, the focusing optics, or both, may alternativelybe incorporated into the light source module. The combined effect of thetwo sets of relay optics (the beam-shaping and the focusing sets) uponthe input beam(s) from the light source(s) is to impart upon the beam(s)the desired output beam propagation characteristics suitable forinterrogating particles. The second set of relay optics then directs thepulsed beam(s) 708 to the flowcell 700. The flowcell 700 provides forthe passage of particles to be analyzed (which can include, withoutlimitation, cells, bacteria, exosomes, liposomes, microvesicles,microparticles, nanoparticles, and natural or synthetic microspheres) byconveying a sample stream 740 containing said particles as a suspension,and a stream of sheath fluid 742 that surrounds and confines said samplestream, as further described herein. An input portion of the flowcellfocuses, e.g., by hydrodynamic means, the sample stream and thesurrounding sheath stream to result in a tight sample core streamflowing through a microchannel portion of the flowcell, surrounded bysheath fluid. The tight sample core stream flowing past theinterrogation region of the flowcell typically exposes, on average, lessthan one particle at a time to the beam or beams for interrogation (thisis sometimes referred to in the art as “single-file” particleinterrogation). The sheath fluid and the sample core stream are directedto a single outlet 744 (and generally discarded as waste) after passagethrough the interrogation portion of the flowcell. As the interrogatingpulsed beam(s) of optical energy (light) interact with particles in thesample core stream by scattering, absorption, fluorescence, and othermeans, optical signals 710 are generated. These optical signals arecollected by relay optics in box 760 (which can include, withoutlimitation, single lenses, doublet lenses, multi-lens elements, mirrors,prisms, optical fibers, or waveguides) positioned around the flowcell,then conveyed to filtering optics in box 760 (which can include, withoutlimitation, colored filters, dichroic filters, dichroic beamsplitters,bandpass filters, longpass filters, shortpass filters, multibandfilters, diffraction gratings, prisms, or holographic optical elements)and then conveyed as filtered light signals 712 by further relay opticsin box 760 to one or more detectors 770 (which can include, withoutlimitation, photodiodes, avalanche photodiodes, photomultiplier tubes,silicon photomultipliers, or avalanche photodiode microcell arrays). Thedetectors convert the optical signals 712 into electronic signals 772,which are optionally further amplified and groomed to reduce the impactof unwanted noise. The electronic signals are sent to an electronicsignal processing unit 790 [which generally comprises a digitizationfront end with an analog-to-digital converter for each signal stream, aswell as discrete analog and digital filter units, and may comprise oneor more of a Field-Programmable Gate Array (FPGA) chip or module; aDigital Signal Processing (DSP) chip or module; an Application-SpecificIntegrated Circuit (ASIC) chip or module; a single-core or multi-coreCentral Processing Unit (CPU); a microprocessor; a microcontroller; astandalone computer; and a remote processor located on a “digitalcloud”-based server and accessed through data network or wired orcellular telephony means], which executes further processing steps uponthe electronic signals. The processed signals 774 are then sent to adata storage unit 792 (which can include, without limitation, aread-only memory unit, a flash memory unit, a hard-disk drive, anoptical storage unit, an external storage unit, or a remote or virtualstorage unit connected to the instrument by means of a wired data ortelecommunication network, a Wi-Fi link, an infrared communication link,or a cellular telephony network link). The stored or preliminarilyprocessed data, or both, can also be made available to an operator foroptional inspection of results.

FIG. 8 illustrates schematically a system configuration of an exemplaryembodiment of the present disclosure, which provides an apparatus forhighly multiplexed analysis and sorting of particles in a sample. Inanother embodiment, it provides an apparatus for lifetime analysis andsorting of particles in a sample. It is similar in configuration to thesystem configuration of FIG. 7, except in that it additionally providesfor the capability to sort and collect particles based on theircharacteristics. The signal processing unit 890 generates in real timesorting control signals 876 based on the presence or absence or degreeor nature of predetermined characteristics of the particles to beanalyzed. For example, it may be desirable to identify and sortparticles that, upon excitation by the interrogating pulsed lightbeam(s), emit fluorescence in a predefined spectral band at a levelabove a predefined threshold. As another example, it may be desirable toidentify and sort particles that, upon excitation by the interrogatingpulsed beam(s), exhibit fluorescence decay curve with a lifetimecomponent in a certain range of values and at a percentage above apredefined threshold. Different criteria may be used in isolation orcombined in compound logical forms (such as AND, OR, NOT, as well asmore complex forms involving numerical comparisons of differentquantities, such as, without limitation, “greater than,” “less than,”and so forth). The processing unit 890, once the processed signals froma given particle meet the predefined set of sorting criteria, triggers asignal 876 conveyed to an actuator driver 894. The actuator driver is anelectronic control module connected to one or more sorting actuators880. The sorting actuators may be positioned in, on, next to, or nearthe flowcell in the vicinity of, and downstream from, the interrogationregion. One or more of the sorting actuators 880 is temporarilyactivated by drive signal 878 from the actuator driver 894 in responseto the triggering signal 876 from the processing unit 890, resulting ina temporary diversion of the sample core stream, or of a portion of thesample core stream, away from the default sorting channel 846 and intoone or more sorting channels 848. The default sorting channel 846optionally directs the fluids it receives into a default receptacle 847.The sorting channel(s) 848 direct the sample core stream, in turn, torespective receiving sorting receptacle(s) 849. Once the temporaryactivation of one or more of the sorting actuators 880 is complete, theactuator(s) return to their resting state, and the sample core streamreturns to its default sorting channel 846. The sorting actuator(s) 880are controllable to achieve multiple actuation states, including,without limitation, with an actuator driver 894, with a built-incontrol, with direct voltage or current control from the processing unit890, or with electrical signals coming directly from logic circuitryconnected with the one or more detectors 870.

In FIGS. 9 and 10, the relative orientation of fluid flow, lightpropagation, and transverse directions is shown, respectively, as theset of axes x, z, and y. The process steps involved in the performanceof some embodiments of the present disclosure are described here inreference to FIGS. 9 and 10, and are also further summarized inflow-chart fashion in FIG. 18A.

FIG. 9 illustrates a cross-section, perpendicular to the direction offluid flow, of a possible light collection configuration of the presentdisclosure. A flowcell 900, of which the inner part is schematicallyindicated in the figure, provides a channel for fluid flow. Sheath fluid920 is provided to confine the fluid 930 carrying particles 955 to beanalyzed. The sheath fluid and the sample-carrying fluid are focusedinto the flowcell lumen, optionally by hydrodynamic means; such focusingproduces a tight sample core stream bounded by the sheath fluid. Aninterrogating light beam or beams 940 are provided to interact with theparticles in the sample core stream. The beam or beams, usually having aGaussian intensity profile, are generally focused into a relativelytight spot in the plane of the sample core stream. Particles to beanalyzed in the sample core stream interact with light in the beam orbeams 940 to generate optical signals 910 by optical processesincluding, for instance, scattering, absorption, or fluorescence. Theoptical signals 910 are collected by collection optics 960. Thecollected optical signals 912 are then conveyed (relayed) to spectralfiltering optics 962 to select appropriate spectral bands of the opticalsignals for detection. The spectral filtering optics 962 may include,without limitation, reflective, transmissive, absorptive, diffractive,or holographic means, or means based on interference, or a combinationthereof. The resulting spectrally filtered optical signals 914 are thenconveyed (relayed) as signals 916 by focusing optics 964 to a detector970. The detector converts the light signals 916 into electrical signals972, which are then conveyed to a processing unit 990 for furtheranalysis, processing, and optionally storage, as described above inreference to FIGS. 7 and 8 and as further discussed below. Together, thecollection optics 960 and the focusing optics 964 may be referred to asrelay optics.

In some embodiments, more than one spectral band output may begenerated. For instance, FIG. 10 illustrates a cross-section,perpendicular to the direction of fluid flow, of another possible lightcollection configuration of the present disclosure. It is similar inconcept to the configuration illustrated in FIG. 9 except that thespectral filtering optics 1062 produce more than one spectral bandoutput 1014 (A and B), separated according to spectral characteristics.Each spectral band is then conveyed (relayed) to a separate set offocusing optics 1064 (A and B) and separate detectors 1070 (A and B),resulting in respectively separate electrically converted signals 1072(A and B). The resulting electrical signals are then routed to signalprocessing unit 1090 for further elaboration. FIG. 10 depicts, for thesake of clarity, two sets of spectral bands, focusing optics, anddetectors; it will be apparent to those skilled in the art that anarbitrary number of such sets is encompassed by the scope of the presentdisclosure.

The process steps described below in conjunction with FIGS. 11A-F, 12Aand 12B are also summarized in a flow-chart fashion in FIG. 18C.

FIG. 11 illustrates, for the specific case of implementation of thepresent disclosure on a flow cytometry platform, the principles involvedin excitation, emission, detection, and analysis of fluorescencelifetime signals from particles under analysis. The various panels ofthe figure will be referred to in the text that follows to betterillustrate the various steps of the signal transduction process. In eachpanel (a)-(f), a graph depicts the evolution over time (t) of certainoptical intensities (I). In every case except where noted, the opticalintensities are each shown as normalized to unit peak values for clarityof illustration; normalization of the optical intensities is preferablein certain embodiments, while in certain other embodiments the opticalintensities are not normalized.

The graph 1110 in FIG. 11A depicts the canonical behavior of the opticalsignals resulting from the interaction between an always-on excitationlight source (also referred to in the art as a constant-wave, or cw,source) and a particle passing through the region of interrogation(typically in a flow cell or other component having a similar function).As the particle enters, then exits, the region of interrogation, theexcitation interaction signal (the dash-dotted line 1115) rises thenfalls, in concert with the spatial profile of the light beam used forinterrogation, as measured along the line of passage of the particle.The particle, in this canonical pedagogical illustration, is assumedsmall in comparison with the dimension of the light beam along thedirection of passage of the particle; modifications to this frameworkthat generalize this to the case of particles of arbitrary size arepossible but are not informative for the purpose at hand, and are nottaken up here. A typical light beam having dimensions, along the line ofpassage of particles to be analyzed, from less than 10 to more than 100microns, and a typical flow cytometer causing particles to pass throughthe region of interrogation at flow velocities of below 0.1 to more than10 m/s, the range of possible durations of the excitation interactionenvelopes, as the shape of curve 1115 in FIG. 11A is sometimes referredto, is quite wide, stretching from less than 1 μs to more than 1 ms.However, in most cases in current practice the full-width athalf-maximum (FWHM) of the excitation interaction envelope is around afew microseconds.

The graph 1120 in FIG. 11B juxtaposes, for illustrative purposes, thecanonical interaction envelope 1115 from FIG. 11A (the dash-dotted line)with one possible configuration of excitation pulses from a modulatedsource of optical energy. The pulses are shown in a train of uniformlyrepeated, substantially identical units (the sharp features 1122 inthick solid lines); each pulse is short as compared to the FWHM of thecanonical interaction envelope, and each pulse is separated fromneighboring pulses by a time generally larger than the width of thepulses themselves. One key aspect of the present disclosure in thispanel 11(b) is that the modulation of the optical energy source (orsources) should result in a series of substantially identical pulses,each short compared to the typical interaction time, and each wellseparated from the next.

The graph 1130 in FIG. 11C depicts the prophetic result of deliveringthe train of excitation pulses 1125 illustrated in FIG. 11B to aparticle flowing in a flow cell according to design and operatingparameters typical of flow cytometer constructions known in the art. Theresulting excitation interactions are shown as a series of pulses ofvarying height (features 1132 in thick solid lines) conforming to anoverall envelope (the dash-dotted line), said envelope corresponding tothe interaction envelope that would result, were the light beamcontinuous instead of pulsed and all other things remaining equal. Whilethe details of the interaction sequence would, generally, vary fromparticle to particle [for example, the detailed timewise location of theindividual interaction pulses 1132 in FIG. 11C under the overallenvelope is a function of the relative timing of the pulse train withrespect to the arrival of the particle], the general nature of theexcitation interaction as consisting of a series of pulses modulated bya “carrier” envelope is determined by the design and operatingparameters of the apparatus. In this graph 1130 of FIG. 11C theindividual interaction pulses are not normalized to unit intensity.

The graph 1140 in FIG. 11D adds another key element of the currentdisclosure to the picture, namely the ability to measure the temporalevolution of the fluorescence decay curves. The overall carrier envelope(dash-dotted line) and the individual excitation interaction pulses(features in thick solid lines) are as illustrated in FIG. 11C. Thefluorescence decay curves are shown as thin dashed lines 1147. Eachfluorescence decay curve follows directly the optical excitationassociated with the interaction pulse immediately preceding it. It canbe appreciated that the fluorescence decay curves are, generally,asymmetric: While the rising portion is dominated by the absorption ofoptical energy from the excitation source, the waning portion (thedecay) is driven by the quantum mechanical processes of fluorescenceemission, which vary from molecule to molecule and are additionallyaffected by the molecular microenvironment, and generally result in acurve with a longer decay-side tail. In this graph 1140 of FIG. 11D theindividual interaction pulses and the individual fluorescence decaycurves are not normalized to unit intensity.

The next process step in the lifetime analysis algorithm involvessegmenting the signal sequence into individual decay curves. The dashedcurves 1147 in graph 1140 of FIG. 11D represent optical emissionsignals, such as, e.g., fluorescence decay curves; these optical signalsare detected by one or more detectors, converted into electrical signal,and digitized for further processing. In graph 1150 of FIG. 11E thesequence of pulse signals (dashed curves, representing digitizedelectrical signals corresponding to the optical signals they areconverted from) is broken mathematically into individual pulse signalsegments 1151-1156 (A, B, C, . . . ) while maintaining a consistentphase across the entire sequence; that is, a selected feature of eachpulse (e.g., the peak, the midpoint of its rising edge, etc.) is chosenas the reference, and the sequence is cut up into equal segments [shownbelow the axis in FIG. 11E], all consisting of substantially the samenumber of digitization elements, and all starting substantially the samenumber L of digitization elements to the left of the respectivereference point, such number L being chosen to result in segments, eachof which (whenever possible) contains an entire decay curve not splitbetween adjacent segments, as illustrated schematically in FIG. 11E. Thesegment length 1167 is chosen to closely match excitation pulse spacing1166.

In an alternate embodiment, the reference points used to cut up thefluorescence signal sequence into substantially equal segments are drawnfrom a reference electronic signal derived from sources that include,without limitation: (i) the sequence of external pulses from amodulation device used to modulate the light source; (ii) a low-jitter,synchronized output function from the modulation device that has thesame repetition frequency as the modulation pulse train; (iii) alow-jitter, synchronized output function from the internally modulatedlight source that has the same repetition frequency as the modulatedpulse train; (iv) the output of a fast photodetector designed to collectlight from the excitation pulse train, e.g., as a “tap” on the mainexcitation beam, from surface scattering on, or partial reflectionsfrom, one or more optical components in the optical path of theexcitation beam; and (v) the output of a fast forward-scatter detector,a fast small-angle scatter detector, a fast intermediate-angle scatterdetector, a fast side scatter detector, a fast backscatter detector, orgenerally any fast photodetector (including, without limitation,photomultiplier tubes, silicon photomultipliers, photodiodes, andavalanche photodiodes) arranged to collect excitation light scatteredelastically by particles in the sample, and optionally designed toreject light inelastically scattered or converted by fluorescenceprocesses; where “fast” means having the ability to convert detectedlight signals into electrical signals with sufficient bandwidth and atsufficient speed to minimize distortion, broadening, and other artifactsin the optoelectronic conversion process and, optionally, thepreamplification and amplification processes. For example, for opticalpulses between 0.5 and 10 nanoseconds in duration, it is preferable tohave a bandwidth greater than 100 MHz, more preferable to have abandwidth greater than 250 MHz, and most preferable to have a bandwidthgreater than 2 GHz; other choices of bandwidth are also possible and maybe desirable, depending on factors including, without limitation,component cost, the electronic noise of the detector/preamplifier at thechosen bandwidth, the degree of jitter present on the signal from othersources, and the electronic characteristics of other components in thesignal detection/amplification/processing path. The reference electronicsignal thus obtained provides a sequence of pulses, from each of which aselected feature (such as, e.g., the peak, the midpoint of the risingedge, etc.) is used as the reference for the demarcation of segmentboundaries in the simultaneously collected fluorescence signal depictedin FIG. 11E.

Graph 1160 in FIG. 11F depicts the following step of signal processingby showing each of the decay curve segments 1151-1156 from FIG. 11E (A,B, C, . . . ) added coherently on top of each other, with the respectivetemporal relationships within each segment unchanged. Such adding isperformed coherently on the basis of individual digitization elements:The values of the first digitization index (#0) in every segment areadded together (A0+B0+C0 . . . ), the values of the second digitizationindex (#1) in every segment are added together (A1+B1+C1 . . . ), and soon for all digitization indices in all segments. The result is a“supercurve” [bold curve 1165 in FIG. 11F], where each digitizationindex has a value equal to the sum of all the corresponding digitizationindices from all segments. The supercurve is then optionally convertedto a semilog scale for further processing.

This signal processing method removes incoherent noise contributionsfrom the result while boosting the contribution of signals coherent frompulse to pulse. The supercurve 1165 in graph 1160 of FIG. 11F may stillexhibit some degree of incoherent noise, which is to be expected giventhe stochastic nature of the decay process and the presence of varioussources of measurement noise on the signals; however, the general natureof the decay is expected to remain constant within a given population offluorophores, and the supercurve process is aimed at maximizing thesignal from such common decay while minimizing the effect of stray lightsignals, electronic noise, and other events lacking information contentgermane to the analysis being carried out.

FIGS. 12A and 12B illustrate exemplary embodiments of several steps ofan analysis method of the current disclosure. Both FIGS. 12A and 12Bdisplay curves plotted on a semilog scale of the natural (or,alternatively, the base-10) logarithm axis of measured intensity vs. thelinear axis of time. In graph 1250 of FIG. 12A the excitation pulse(bold solid curve 1201) and the resulting emission supercurve due, e.g.,to fluorescence (dashed curve 1210) are each shown as normalized tounity peak value for clarity of illustration; on the shown logarithmicscale, a linear value of one corresponds to the logarithmic value ofzero. Normalization of the emission supercurve is preferable in certainembodiments, while in certain other embodiments the supercurve is notnormalized. The supercurve 1210 shown is obtained as described above inreference to FIGS. 11A-F for supercurve 1165. For illustrative purposes,the supercurve 1210 in graph 1250 of FIG. 12A is shown as comprisingthree distinct lifetime components [each also referred to herein as“component,” “lifetime,” “lifetime value,” “1/e value,” “time constant,”“decay constant,” or “exponential decay”, and corresponding to the valueof τ in the standard exponential decay formula I(t)=I₀ exp (−t/τ), whereI₀ is the starting intensity and I(t) is the intensity after a time t]:τ_(a), τ_(b), and τ_(c). In this example, τ_(a) is the smallest timeconstant of the three, τ_(c) is the largest, and τ_(b) is intermediatebetween the two. The relative values of τ_(a), τ_(b), and τ_(c) arereflected in the slopes of the three branches a (1221), b (1222), and c(1223) of the supercurve 1210: the slope of branch a 1221 is steepestthe slope of branch c 1223 is mildest, and the slope of branch b 1222 isintermediate between the two. The slope of a branch on a semilog plot ofthe kind depicted in FIGS. 12A and 12B is inversely proportional to thevalue of the corresponding time constant. The three branches 1221-1223of the supercurve 1210 in FIG. 12A are defined as follows: The firstbranch a 1221 begins at the peak 1231 of the supercurve and ends at thefirst “knee” 1232 of the supercurve (where by “knee” is meant asubstantial change in slope, indicated by an open circle); the secondbranch b 1222 begins at the first knee 1232 and ends at the next knee(the next open circle 1233); the third branch c 1223 begins at this nextknee 1233 and ends at 1234 where the supercurve meets the measurementnoise floor (schematically indicated in FIG. 12A by the time axis t).The slope of each branch is defined as customary as the ratio of theordinate and the abscissa over a portion of or the entire branch: e.g.,for branch b, the slope value is s_(b)=y_(b)/t_(b). The time constantcorresponding to such slope is then obtained by the reciprocal of theslope: τ_(b)=1/s_(b).

It will be appreciated that when dealing with real measurements subjectto effects including, without limitation, noise, background,uncertainty, instrument error or drift, component variability, and/orenvironmental effects, there may be departures, sometimes substantial,from the illustrations and depictions presented here. Even when sucheffects are low or minimized, other effects may act to mask, distort,alter, modify, or otherwise change the relationships among the variousmathematical and physical quantities mentioned here. As one example, thenoise floor where the supercurve in FIG. 12A starts and ends may behigher in certain cases and lower in others, depending on severalfactors, including, without limitation, the ones just mentioned. Thevariability in the noise floor may affect the determination of one ormore of the slopes of the branches of the supercurve. Likewise, theprecise location of a knee between two branches on the supercurve may besubject to uncertainty depending on the level of residual noise on thesupercurve. Depending on the digitization sampling rate and on the noisepresent on the signal, the transition from one slope to the next (theknee) may occur more gradually than over a single data point, forexample over two, three, or more data points. Another example of thedistortion created by physical effects is shown in FIG. 12A where brancha 1221 is shown as beginning at the peak 1231 of the supercurve 1210,however the slope of this first branch does not immediately convergeonto a stable value due to the roll-off from the peak. The degree ofroll-off is dependent on the shape of the excitation pulse, the value ofthe first-branch lifetime, and other factors. These effectsnotwithstanding, one improvement of the present disclosure is used tominimize the impact of such effects. Construction of the supercurve froma number of individual pulse signals, with its attendant improvement insignal to noise, is one element that contributes to such minimization.

Another element is the relative simplicity in the extraction of desiredparameters, such as the values of the time constants, from a supercurve.This is illustrated by graph 1280 in FIG. 12B. Graph 1280 shows a detail1211 of the supercurve 1210 of FIG. 12A, where branch a 1281(corresponding to branch a 1221 in graph 1250) ends and branch b 1282(corresponding to branch b 1222 in graph 1250) begins. (The graph hasbeen offset and rescaled in both abscissa and ordinate for illustrativepurposes.) The two branches meet at the knee 1292 indicated by the opencircle, corresponding to knee 1232 in graph 1250. Also plotted in FIG.12B are the individual digitized points of the supercurve, indicated bysmall filled circles with solid drop lines to the time axis. The processstep of determining the location of a knee (that is, the transitionbetween one branch where a value of lifetime dominates, to anotherbranch where a different value of lifetime dominates) comprisescomputing differences between successive values of the digitizedsupercurve. Four such difference for branch a are shown as δ_(a). Whereresidual noise on the supercurve is minimized, the value of δ_(a) fromdigitized point pair to digitized point pair will show little variation.Once the knee is crossed, however, the next computed difference willjump to δ_(b), and successive differences will once again remainsubstantially uniform around this new value. For one of the mainimprovements of the present disclosure, namely the provision of highlymultiplexed means of particle analysis and sorting, it is not criticalthat the depicted successive values of δ_(a) be rigorously constant, northose of δ_(b); it is merely sufficient that δ_(a) be different enoughfrom δ_(b) to enable detection of the slope change at, or within areasonably narrow range of, the indicated knee point. Sufficientdifference between δ_(a) and δ_(b) is related to the precision andaccuracy of the measurement system, the number, types, and severity ofnoise or error sources, and other factors. Detection of a discontinuouschange in slope, however, is intrinsically simpler, instrumentally andcomputationally, than the absolute determination of the value of aslope.

Once a knee is found, the process continues until the entire supercurveis examined. The location of each knee, together with the location ofthe start of the first branch and the end of the last branch, define allthe branches of the supercurve. The next processing step involvescomputing the average slope for each branch, which was described abovein reference to FIG. 12A, and from such slope values the time constantsof each branch are calculated. The following processing step involvesallocating each branch to one of a set of predetermined lifetime (ortime constant) bins. As illustrated in FIGS. 5 and 6, one aspect of thepresent disclosure is the provision of a limited set of lifetime bins,where the lifetime within any one bin is allowed to vary somewhat, aslong as the variation is not greater than the difference betweenneighboring bins. For the purpose of analyzing a supercurve anddetermining what lifetimes gave rise to the signals from which thesupercurve was constructed, it is sufficient to establish (1) which ofthe lifetime bins was present in the measured particle or event (i.e.,what fluorophores or other molecular species were present with afluorescence decay value within the range of any one of the providedlifetime bins), and (2) the degree of relative contribution of eachdetected lifetime. For (1), the set of time constants computed from thebranches of the supercurve as described above is compared to the set ofallowed lifetime bins. In some cases there will be as many separatedetected branches in a supercurve as there are bins: this would be thecase for FIG. 12A, for example, if the number of allowed bins were 3. Inother cases there will be fewer branches, indicating that a certain binwas not present (i.e., that no fluorophore or molecular species with alifetime in the range of values of that bin was detected). By comparingthe set of measured time constants with the set of allowed bins, adetermination is made as to which bins are present in the measurement.Determination of the relative contribution of each detected lifetime(now associated with one of the allowed lifetime bins) is performed bycomparing the values of the ordinates of each branch [the values y_(a),y_(b), and y_(c) in graph 1250 of FIG. 12A] with a calibration look-uptable generated during manufacture of the apparatus. Such calibrationlook-up table is created by generating supercurves with known inputs,i.e., with 100% of one lifetime bin, 100% of another, and so on for allthe lifetime bins selected to be available on the apparatus; then withvarying mixtures of bins, such as, e.g., 10% of bin 1 and 90% of bin 2,20% of bin 1 and 80% of bin 2, and so on until 90% of bin 1 and 10% ofbin 2; repeating this for each pair of bins available on the apparatus.The resulting data provides the look-up table to compare measuredlifetime ordinates (e.g., y_(a), y_(b), and y_(c)) with, and therebydetermine the relative contributions of each detected lifetime.

Another exemplary embodiment of the present disclosure involvesperforming curve fitting on the constructed supercurve. The fittingprocedure (using methods that include, without limitation, linearregression, nonlinear regression, least squares fitting, nonlinear leastsquares fitting, partial least squares fitting, weighted fitting,constrained fitting, Levenberg-Marquardt algorithm, Bayesian analysis,principal component analysis, cluster analysis, support vector machines,neural networks, machine learning, deep learning, and/or any of a numberof other numerical optimization methods well known in the art) isdesigned to determine the most likely combination of lifetimes andcontributions resulting in the observed supercurve. In the case ofhighly multiplexed analysis and sorting of particles based on lifetimeanalysis, an instrument is generally designed with a fixed number ofknown, discrete lifetime bins. Therefore the fitting procedure does notrequire the determination of the lifetimes, but simply of thecontributions of each lifetime component to the observed signal. Thismakes the fitting procedure much more constrained than would normally bethe case, and this in turn makes the determination of lifetimecontributions faster and computationally less expensive. In thisexemplary embodiment, extraction of slopes from the supercurve,identification of each knee present in the supercurve, and determinationof lifetime contributions from lookup tables are all replaced by thefitting procedure; the outcome of the fitting procedure is the best-fitset of contributions to the observed signals from each potentiallycontributing lifetime bin. In certain cases, the contributions from one,or more, or from all but one, or from all lifetime bins may bedetermined by fit to be zero or substantially zero; in certain cases,the fit may produce substantially nonzero contributions from one, frommore than one, or from all lifetime bins. In the case of an apparatusfor lifetime analysis of particles in a sample, where the lifetime orlifetimes are not known a priori, the fitting procedure would includedetermination of the lifetime values, as well as of the contributions ofeach lifetime to the observed signal.

Yet another embodiment of the present disclosure involves computing thearea under the supercurve, and comparing the result with one or morelookup tables. FIGS. 20A-C illustrate this procedure for three exemplarycases of a multiplexed system with two lifetime bins: (a) the case of asupercurve with only a short-lifetime (τ_(a)) decay, (b) the case of asupercurve with only a long-lifetime (τ_(b)) decay, and (c) the case ofa supercurve with a two-lifetime (τ_(a) and τ_(b)) decay. Each of FIGS.20A-C displays curves plotted on a linear scale of measured intensity Ivs. the linear axis of time t. Each graph 2050, 2051, and 2052 of FIGS.20A-C, respectively, shows the emission supercurve due, e.g., tofluorescence, as dashed curve 2010. The curves in these graphs are shownafter baseline correction (which step is described below), and afternormalization to unit peak height (which step is described below), forclarity of illustration; the raw measured intensity supercurves cangenerally take on a range of values of both baseline and peak height.The area under each curve is shown as the shaded regions 2035, 2036, and2037 in FIGS. 20A, B, and C, respectively. In these Figures, the area isaccumulated starting from the time 2031 when the supercurve 2010 reachesits peak value, and continuing through the rest of the supercurve. In analternative embodiment, the entire area under the supercurve, includingthe unshaded area under the supercurve before the time 2031 when thesupercurve reaches its peak value, is computed. In another embodiment,the area under the supercurve is computed starting from a timesubsequent to the start of the supercurve but prior to the time 2031when the supercurve 2010 reaches its peak value, and continuing throughthe rest of the supercurve. In yet another embodiment, the area underthe supercurve is computed starting from a time subsequent to the time2031 when the supercurve 2010 reaches its peak value, and continuingthrough the rest of the supercurve. In yet another embodiment, the areaunder the supercurve is computed starting from a time prior to orsubsequent to the time 2031 when the supercurve 2010 reaches its peakvalue, and continuing to a second, later time prior to the end of thesupercurve.

The area under the supercurve can be efficiently computed bysequentially adding successive measured intensity values of thesupercurve, i.e., the values corresponding to exemplary digitizedsampling points 2071 for the supercurve illustrated in FIG. 20A; thevalues corresponding to exemplary digitized sampling points 2073 for thesupercurve illustrated in FIG. 20B; and the values corresponding toexemplary digitized sampling points 2075 for the supercurve illustratedin FIG. 20C. While only sets of three digitized sampling points areshown illustratively in each Figure, it will be readily apparent tosomeone of ordinary skill in the art that a supercurve may comprise manysuch points, as schematically indicated by the ellipses in each Figure,such as, e.g., tens, hundreds, or more points. To increase the accuracyof the value of the area under the supercurve, standard methods ofnumerical integration as are well known in the art may be optionallyemployed, including, without limitation, the trapezoidal rule,quadrature, splines, and interpolation. In an embodiment, the supercurveis not baseline-corrected, i.e., the sum of intensity values is notadjusted by subtracting a baseline. In another embodiment, thesupercurve is baseline-corrected prior to the start of the areacomputation, i.e., a value corresponding to its baseline is subtractedfrom each measured intensity value to yield a supercurve with thecorrected, zero-baseline 2038 illustrated in FIG. 20A. In anotherembodiment, the supercurve is baseline-corrected after adding thedigitized sampling values together, i.e., a value corresponding to itsbaseline multiplied by the number of added digitized sampling values issubtracted from the sum of intensity values to yield a supercurve withthe corrected, zero-baseline 2038 illustrated in FIG. 20A. Baselinecorrection is performed using any of a number of methods well known inthe art, including filtering, signal conditioning, analog signalprocessing, digital signal processing, and hybrid signal processingmethods. In an embodiment, the sum of intensity values (whetherbaseline-corrected or not) is then optionally multiplied by the samplinginterval 2039 (δt) to yield the areas 2035, 2036, and 2037,respectively, in each of the exemplary cases illustrated in FIGS. 20A,B, and, C. In another embodiment, for greater computational efficiency,this step can be skipped, as long as the values in the lookup tablesthat the measured area is compared to are generated in an analogous way;to avoid confusion, in what follows we will refer to “area under thesupercurve” (alternatively, “supercurve area”) interchangeably as eitherthe sum of intensity values, or such sum multiplied by the samplinginterval 2039. The computed area under the supercurve is then normalizedby dividing it by the raw measured peak value reached at time 2031.

The computed normalized value of the area under the supercurve is thencompared with values in a lookup table constructed to cover a specifiedrange of possible supercurve area values that can be obtained in ameasurement. Such a lookup table includes relatively low values of thearea, such as area 2035 depicted in FIG. 20A; relatively high values ofthe area, such as area 2036 depicted in FIG. 20B; and relativelyintermediate values of the area, such as area 2037 depicted in FIG. 20C.For the exemplary case illustrated here of a multiplexed system with twolifetime bins, the lookup table furnishes, for each value of supercurvearea, a corresponding pair of values: (i) the relative contribution ofthe short-lifetime (τ_(a)) component of decay, and (ii) the relativecontribution of the long-lifetime (τ_(b)) component. In the caseillustrated in FIG. 20A, the lookup table would provide a relativecontribution of the short-lifetime component of 100% or approximately100%, and a relative contribution of the long-lifetime component of 0%or approximately 0%; in the case illustrated in FIG. 20B, the lookuptable would provide a relative contribution of the short-lifetimecomponent of 0% or approximately 0%, and a relative contribution of thelong-lifetime component of 100% or approximately 100%; in the caseschematically illustrated in FIG. 20C, the lookup table would provide arelative contribution of the short-lifetime component of 50% orapproximately 50%, and a relative contribution of the long-lifetimecomponent of 50% or approximately 50%. The actual values of eachrelative contribution are provided in the lookup table previouslygenerated in a calibration process, where reference supercurve areavalues are obtained from sets of samples with only a short-lifetimecomponent, only a long-lifetime component, and known mixtures of bothcomponents in varying ratios, from ratios approaching pureshort-lifetime component (such as, e.g., 3,000:1, 10,000:1, or higher)to ratios approaching pure long-lifetime component (such as, e.g.,1:3,000, 1:10,000, or higher), through intermediate ratios (such as,e.g., 1,000:1, 300:1, 100:1, 30:1, 10:1, 3:1, 1:1, 1:3, 1:10, 1:30,1:100, 1:300, and 1:1,000). A lookup table may be provided withrelatively few, such as, e.g., 10 or less, values of supercurve area;relatively many, such as, e.g., 1,000,000 or more, values of supercurvearea; or a relatively intermediate number, such as, e.g., 100, 1,000,10,000, or 100,000, values of supercurve area.

The process of comparing the measured supercurve area with values in thelookup table may comprise finding the closest value in the table,interpolating linearly between the two closest values, interpolatingquadratically between the two closest values, extrapolating beyond thefirst or last value linearly, quadratically, or based on higher-orderpolynomial or on exponential curves, or any number of additionalnumerical estimation processes well known in the art. Once the closestvalue in the lookup table (or an interpolated or extrapolated value) isfound, the corresponding pair of relative contributions (or thecorresponding pair of interpolated or extrapolated relativecontributions) of each lifetime component is obtained. Each relativecontribution is then multiplied by the peak intensity value of thesupercurve reached at time 2031 in order to obtain the absoluteintensity contributions of each lifetime component to the measuredsignal (these values are equivalent to ones known in the art as “height”or “peak” signal values). Each relative contribution is optionallymultiplied by a factor Sin order to obtain the absolute areacontributions of each lifetime component to the measured signal (thesevalues are equivalent to ones known in the art as “area” signal values):the factor S is calculated by adding the peak values of the raw signalpulses [i.e., pulses 1151, 1152, . . . , 1156 in FIG. 11E] andmultiplying the result by pulse-to-pulse spacing 1166. To increase theaccuracy of computation of the “area” signal values, standard methods ofnumerical integration as are well known in the art may be optionallyemployed in determining factor S, including, without limitation, thetrapezoidal rule, quadrature, splines, and interpolation. For theavoidance of confusion, the “area” signal values referred to here referto an estimation of the area under the total envelope 1115 ofinteraction between a particle and the light beam in FIG. 11A, whereasthe “area under the supercurve” refers to an estimation of the areaunder supercurve 1165 in FIG. 11F.

While certain embodiments described herein used an exemplaryconfiguration of a multiplexing system with two lifetime bins, it willbe readily apparent to someone of skill in the art that otherconfigurations, including ones with three, four, or more lifetime bins,are encompassed by this disclosure. While the illustrative exemplardescribed herein is based on representing and operating on supercurves,summed intensity values, and areas on linear scales, alternativeembodiments comprise representing and operating on supercurves, summedintensity values, and areas on logarithmic or other scales. It will alsobe readily apparent to someone of skill in the art that while a specificexample is given of a method involving a lookup table generated with twospecific lifetime components (τ_(a) and τ_(b)) in varying ratios, asystem of the current disclosure may also be provided with two, three,or more separate lookup tables, each table referring to a combination ofdifferent lifetimes (including, without limitation, for the case of twolifetimes: a common short lifetime τ_(a) and a different long lifetimeτ_(c); a common long lifetime τ_(b) and a different short lifetimeτ_(d); a different short lifetime τ_(e) and a different long lifetimeτ_(f); and additional similar such combinations). The system may beprovided with an option for the user to select a certain combination oflifetimes based on the type of assay being performed, or the system maybe provided with a fixed configuration of certain lifetimes.Furthermore, each detection channel may be provided with the same lookuptable, or each detection channel may be provided with a different lookuptable (or a set of lookup tables) based on the combination of lifetimesassigned by default to, or selectable by the user for, that channel.

FIGS. 13A-B illustrate exemplary embodiments of two steps of an analysisand sorting method of the current disclosure. In FIGS. 13A-B, therelative orientation of fluid flow, light propagation, and transversedirections is shown as the set of axes x, z, and y, respectively. Theassignment of the axes and directions is similar to that in FIGS. 9 and10, however the orientation of the axes with respect to the page isrotated as compared to FIGS. 9 and 10, with the light propagation andflow directions being in the plane of the page in FIGS. 13A and B. Eachof the two figures shows a schematic representation of a side view ofthe interrogation region 1331 and sorting region 1332 of the flowcell1300. The focusing region of the flowcell, if provided, e.g., byhydrodynamic means, is to the left of the picture; the sample corestream 1330, surrounded by the sheath fluid 1320, comes in from the leftand flows towards the right. The sheath fluid 1320 is bounded by theinner walls of the flowcell 1300, and the sample core stream 1330 isbounded by the sheath fluid 1320. In the interrogation region 1331 atleft, one or more beams of pulsed optical energy 1340 are delivered tothe flowcell by relay/focusing optics and intersect the sample corestream 1330. In the sorting region 1332 at right, one or more actuators(shown in the picture as actuator 1380) are provided in contact with ornear the flowcell, positioned in such a way as to overlay the positionof the sample core stream 1330.

FIG. 13A shows a first time step in the processing of a sample whereby asingle particle 1355 in the sample core stream 1330 enters theinterrogation region 1331 (where the beam or beams 1340 intersect thesample core stream 1330). The light-particle interaction generates lightsignals as described above in reference to FIG. 9 or 10, which lightsignals are collected and relayed to one or more detectors. Thedetector(s) record the optical interaction signals generated by theparticle 1355, and convey that information to the signal processing unitas illustrated schematically in FIG. 8. As described above in referenceto FIG. 8, the processing unit uses that information to produce, ifcertain predetermined criteria are met, a triggering signal for anactuator driver, which driver in turn activates the actuator 1380 inFIG. 13A. FIG. 13B shows a second time step in the processing of thesample whereby the particle 1355 detected in the step depicted in FIG.13A, after following path 1365 in the flowcell along direction x,arrives at a point in the vicinity of the actuator 1380 in the sortingregion 1332 of the flowcell. The design of the flowcell, of the opticallayout of the actuator, and of the detection, processing, and controlelectronics is such that the actuator is activated as such a time whenthe passing particle is calculated, estimated, predicted, or found, uponcalibration or determined empirically during instrument design orassembly, to be nearest to a position where activation of the actuatorresults in the desired diversion of the core stream to one of the one ormore sorting channels. The timing of the triggering signal (i.e., therelative delay from particle detection to sorting actuation) is designedto take into account both the average velocity of fluid flow in theflowcell and its spatial profile across the flowcell cross-section,according to the characteristics of Poiseuille flow known in the art andas modified based on empirical or modeling information.

In FIGS. 14A and B, 15A and B, 16A and B, and 17A-D, the relativeorientation of fluid flow, light propagation, and transverse directionsis shown as the set of axes x, z, and y, respectively. The assignment ofthe axes and directions is similar to that in FIGS. 9 and 10, howeverthe orientation of the axes with respect to the page is rotated ascompared to FIGS. 9 and 10, with the fluid flow and transversedirections being in the plane of the page in FIGS. 14A and B, 15A and B,16A and B, and 1A-D. The cross-sectional plane depicted in FIGS. 14A andB, 15A and B, 16A and B, and 17A-D is the plane that contains the samplecore stream.

FIGS. 14A and 14B illustrate one embodiment of two states of theparticle analysis and sorting method of the current disclosure. Each ofthe two figures shows a schematic representation of a cross-sectionalview of the sorting region of the flowcell. Similarly to the situationdepicted in FIGS. 13A and 13B, the focusing region of the flowcell,e.g., by hydrodynamic means, if provided, is to the left of the picture;the sample core stream 1430, surrounded by the sheath fluid 1420, comesin from the left and flows towards the right. The sheath fluid 1420 isbounded by the inner walls of the flowcell 1400, and the sample corestream 1430 is bounded by the sheath fluid 1420. The flowcell 1400splits into two channels in the sorting region: the default sortingchannel 1446 and the sorting channel 1448. Actuator 1480 is depicted asembodied in, in contact with, or in proximity of the inner wall of theflowcell 1400 on the default sorting channel side. FIG. 14A shows theconfiguration of the default state, where with the actuator 1480 in theOFF state, a non-selected particle 1450 in the sample core stream 1430flows by design into the default sorting channel 1446. Similarly to thestate depicted in FIG. 13B, FIG. 14B shows the configuration of thesorting state, where with the actuator 1480 in the ON state, a transientgas, vapor, or gas-vapor bubble, or a region of heated or cooled,less-dense sheath fluid 1495 is generated (by means including, withoutlimitation, thermal means, electrolytic means, and/or gas injectionmeans), which creates a localized flow diversion in the depictedcross-sectional plane and in its vicinity, which diversion temporarilydeflects the sample core stream 1431 into the sorting channel 1448,which sample core stream contains a particle 1455 detected upstream andautomatically selected by analysis algorithms to trigger sortingactuation. Following deactivation of the actuator 1480, the transientgas, vapor, gas-vapor bubble or region of less-dense fluid 1495 shrinksor is cleared away, and the flow pattern returns to the original defaultstate of FIG. 14A.

FIGS. 15A and 15B illustrate another embodiment of two states of aparticle analysis and sorting method of the current disclosure. It issimilar to the embodiment illustrated in FIGS. 14A and 14B, except inthe design and nature of actuation. Here the actuator 1580 is located inproximity to an expandable chamber 1597 adjacent to the flowcell innerwall and separated from the sheath fluid 1520 by a flexible membrane1596. With the actuator 1580 in the OFF or default state as shown inFIG. 15A, the expandable chamber 1597 is in its default configuration ata pressure designed to match the pressure of the fluid inside theflowcell at the location of the membrane, resulting in a flat shape ofthe membrane to match the shape of the flowcell inner wall, and anon-selected particle 1550 in the sample core stream 1530 flows bydesign into the default sorting channel 1546. With the actuator 1580 inthe ON or sorting state as shown in FIG. 15B, the expandable chamber1597 is pressurized (by means including, without limitation, thermalmeans, mechanical means, hydraulic and/or gas injection means) to ahigher pressure than in the default configuration; this pressuredifferential causes the membrane 1596 to flex into the flowcell until anew equilibrium is reached. The bulging membrane causes the flow patternto shift in a similar way to that previously shown for FIG. 14 B,resulting in the sample core stream 1531 being temporarily diverted intothe sorting channel 1548, which sample stream contains a particle 1555detected upstream and automatically selected by analysis algorithms totrigger sorting actuation. Following deactivation of the actuator 1580,the expandable chamber 1597 is allowed to or made to return to itsdefault pressure state, the membrane 1596 returns to its default flatshape, and the flow pattern returns to the original defaultconfiguration of FIG. 15A.

FIGS. 16A and 16B illustrate yet another embodiment of two states of aparticle analysis and sorting method of the current disclosure. It issimilar to the embodiment illustrated in FIGS. 15A and 15B, except inthe design of actuation. Sorting actuation here is realized by means oftwo actuators, positioned on opposite sides of the flowcell, eachactuator being located in proximity to expandable/compressible chambers(1697 for the default side and 1699 for the sort side) adjacent to theflowcell inner wall and separated from the sheath fluid 1620 by aflexible membrane (1696 for the default side and 1698 for the sortside). In the default state, depicted in FIG. 16A, the expandablechambers 1697 and 1699 of both the default-side and sort-side actuatorsare in their default configuration at pressures designed to match thepressure of the fluid inside the flowcell at the location of themembranes 1696 and 1698, resulting in flat shapes of the membranes tomatch the shape of the flowcell inner walls. In this non-sorting state,a non-selected particle 1650 in the sample core stream 1630 flows bydesign into the default sorting channel 1646. In the sorting state,depicted in FIG. 16B, the expandable chamber 1697 of the default-sideactuator 1680 is pressurized (by means including, without limitation,heating means, mechanical means, hydraulic means, and/or gas injectionmeans), through actuation, in a similar way as depicted in reference toFIG. 15B; this pressure differential with respect to the local pressurein the sheath fluid causes the membrane 1696 to bulge into the flowcelluntil a new equilibrium is reached. Simultaneously, the compressiblechamber 1699 of the sorting side actuator 1681 is depressurized (bymeans including, without limitation, cooling means, mechanical means,hydraulic means, and/or gas aspiration means), through activation ofactuator 1681, to a lower pressure than in the default configuration;this pressure differential with respect to the local pressure in thesheath fluid causes the membrane 1698 to flex away from the flowcelluntil a new equilibrium is reached. The combination of the inwardlybulging default-side membrane 1696 and the outwardly flexing sort-sidemembrane 1698 causes the flow pattern to shift in a similar way to thatpreviously shown for FIGS. 14B and 15B, resulting in the sample corestream 1631 being temporarily diverted into the sorting channel 1648,which sample stream contains a particle 1655 detected upstream andautomatically selected by analysis algorithms to trigger sortingactuation. Following deactivation of the actuator pair, both thedefault-side and the sort-side expandable/compressible chambers 1697 and1699 are allowed to or made to return to their default pressure states,both the default-side and the sort-side membranes 1696 and 1698 returnto their default flat shapes, and the flow pattern returns to theoriginal default configuration of FIG. 16A.

FIGS. 17A-D illustrate a multi-way sorting embodiment of a particleanalysis and sorting method of the current disclosure. Each of the fourfigures shows a schematic representation of a cross-sectional view ofthe sorting region of the flowcell. The configuration is similar to thatdepicted in reference to FIGS. 14A and 14B, except that instead of asingle sorting channel, a plurality of sorting channels 1741-1744 isprovided along a transverse direction y. One advantage of thisembodiment is the ability to have a plurality of different receptaclesinto which the sample may be sorted, depending on the result of theupstream analysis by the interrogating light beam, the signal detectors,and associated electronic and logic trigger circuitry. For example, thesignals detected in response to the upstream interrogation of the samplemay indicate that a particle, e.g., particle 1751, was detected with acertain set A of properties targeted for selection (e.g., the presenceof surface antigens or intracellular markers associated with certainkinds of cancer cells). It may be desirable to sort particles havingthese properties into a certain collection receptacle, e.g., oneprovided to receive the outflow from sorting channel 1741, asillustrated in FIG. 17B. Another particle, e.g., particle 1752, may flowpast the interrogation point and produce signals that indicate thepresence of a different set B of properties targeted for selection(e.g., the presence of surface antigens or intracellular markersassociated with certain kinds of stem cells). It would be desirable tosort particles like particle 1752 having set-B properties into adifferent receptacle from that designed for collection of particleshaving set-A properties: e.g., a receptacle provided to receive theoutflow from sorting channel 1742, as illustrated in FIG. 17C. Likewisefor yet another set D of properties, particles like particle 1754detected as having those properties, and a sorting channel 1744 designedto flow into a receptacle to collect such particles. Accordingly, theembodiment illustrated in FIGS. 17A-D provides an example of such amulti-way sorting capability of the current disclosure, with a number ofsorting channels 1741-1744 in addition to the default sorting channel1746. FIGS. 17A-D exemplarily show four such sorting channelsexplicitly. It will be apparent to those skilled in the art thatadditional configurations having more or less than four sortingchannels, in addition to the default sorting channel, do not depart fromthe scope of the current disclosure.

Each of the sorting channels 1741-1744 (as well as the default sortingchannel 1746) may optionally be connected with a receiving receptacledesigned to collect the fluid flow from the respective channel. Theselection of a particular sorting channel (or of the default sortingchannel) as the target for reception of a desired sorted portion of thesample core stream is effected by actuation of actuator 1780. In atwo-way sort there are two principal sorting states, which can bedescribed as OFF (default) and ON (sorting) as described above inrelation to FIGS. 14A-B, 15A-B, and 16A-B. In a multi-way sort, on theother hand, there generally can be as many sorting states as there aresorting “ways” or possible sorting channels. With reference to FIGS.17A-D, five possible sorting channels are indicated (the default sortingchannel 1746 plus four sorting channels 1741-1744); accordingly, this isreferred to as a five-way sort. An actuation process is provided toresult in different degrees of deflection of the sample core streamportion, corresponding to the selection of different intended sortingchannels.

In FIG. 17A actuator 1780 is depicted as embodied in, in contact with,or in proximity of the inner wall of the flowcell 1700 on the defaultsorting channel side. Similarly to the state depicted in FIG. 14A, FIG.17A shows the configuration of the default state, where with theactuator 1780 in the OFF state, a non-selected particle 1750 in thesample core stream 1730 flows by design into the default sorting channel1746. Similarly to the state depicted in FIG. 14B, FIGS. 17B-D show theconfigurations of various sorting states, where with the actuator 1780in the ON state at levels 1, 2, and 4, respectively, transient regions1791, 1792, and 1794, respectively (comprising, without limitation, agas, vapor, gas-vapor bubble, or a less-dense region of sheath fluid),are generated (by means including, without limitation, thermal means,electrolytic means, and gas injection means), which create respectivelocalized flow diversions in the depicted cross-sectional plane and inits vicinity, which diversions temporarily deflect the sample corestream into configurations 1731, 1732, and 1734, respectively, and causethe corresponding particles 1751, 1752, and 1754, respectively, to flowinto the respective sorting channels 1741, 1742, and 1744. Followingdeactivation of the actuator, the transient gas bubble shrinks or iscleared away, and the flow pattern returns to the original default stateof FIG. 17A. Not shown is the configuration of a sorting stateintermediate to the sorting states of FIGS. 17C and 17D, correspondingto an actuation level 3, whereby a transient region of a sizeintermediate between that of regions 1792 and 1794 temporarily divertsthe sample core stream into sorting channel 1743.

Throughout this disclosure the term “default sorting channel” isassociated with an OFF state of an actuator, signifying a passive statein which no particle sorting is performed, and in which the sample corestream and particles therein are typically outflowed and discarded asundesired waste. The term “sorting channel” is associated with an ONstate of an actuator, signifying an activated state of an actuator, inwhich active sorting of a desired particle is performed. While for someembodiments this may be a preferred configuration, the currentdisclosure is not so limited, and included under the scope of thedisclosure are embodiments where a passive state of an actuator isassociated with collection of desired particles, and an active state ofan actuator is associated with generation of a waste stream of undesiredparticles from the particle analyzer/sorter.

Referring to FIG. 18B, a flow chart is provided that describes asequence of principal steps involved in the performance of the step ofparticle analysis in accordance with an embodiment of the presentdisclosure. A first step 1840 involves the generation of one or moretrains of optical pulses (or other modulated output of light from one ormore sources) for optical interrogation of particles in a sample. Asecond step 1842 involves the presentation of a sample, or theintroduction of a sample, to the apparatus by a user or operator. Athird, optional, step 1844 involves the mixing, reaction, and incubationof the sample with one or more reagents, which reagents may be preloadedonboard the apparatus or may be introduced by the user or operator. Afourth step 1846 involves the formation, by means of hydrodynamicfocusing of the sample by sheath fluid, of a core stream of particlesflowing essentially in single file in the microchannel portion of theflowcell for optical interrogation. A fifth step 1848 involves theinterrogation, by optical interaction, of a single particle in thesample core stream by the one or more trains of optical pulses,resulting in the generation of optical interaction signals. A sixth step1850 involves the collection of the optical interaction signals, theoptical filtering of the collected optical signals, and the spectralisolation of the filtered optical signals. A seventh step 1852 involvesthe detection of the spectrally isolated optical signals, thetransduction of said signals into analog electrical signals, thedigitization of the analog electrical signals into digital signals, andthe processing of the digital signals. An eighth step 1854 involves thefurther application of digital signal processing algorithms to thedigital signals corresponding to each isolated spectral band so as toisolate the separate contributions of one or more fluorescence lifetimecomponents to each signal. A ninth optional step 1856 involves thecoherent summing, or coherent averaging, of lifetime signals coming fromdifferent pulses but all originating from the same particle underinterrogation. A tenth step 1858 involves the recording and storage ofthe detected and processed signal parameters, including, withoutlimitation, fluorescence intensity in one or more spectral bands, one ormore fluorescence lifetime values in each of the one or more spectralbands, phase shift, scattering intensity, and absorption. An eleventhstep 1860 involves a decision, which may be automated or may bepresented by the system, through a processing unit, to the user oroperator as a call for action, on whether to analyze additionalparticles; if the choice is positive, the method workflow returns to thefifth step above; if the choice is negative, the method workflowcontinues to the next (twelfth) step below. A twelfth optional step 1862involves the classification of a portion or a totality of the eventsdetected and analyzed according to certain criteria (which may include,without limitation, entities commonly referred to in the art as“triggers,” “thresholds,” and “gates”), which may be predetermined andpreloaded into the apparatus or may be selected or modified or createdby the user. A thirteenth step 1864 involves the presentation to theuser or operator of the processed data (which may include, withoutlimitation, the raw detected time-varying signals, a list of detectedparticle-interrogation events, and graphs or plots of detected eventsdisplayed according to characteristics such as, e.g., fluorescencelifetime, fluorescence intensity, and scattering) by means of a userinterface such as, e.g., a screen, a computer monitor, a printout, orother such means.

FIG. 19 shows a block diagram of an exemplary embodiment of a dataprocessing system 1900 to provide a particle analysis and sorting systemas described herein. In an embodiment, data processing system 1900 is apart of the control system to perform a method that includes providinglight pulses; providing a sample for analysis; exposing the sample tothe light pulses; detecting optical decay curves; and extracting timeconstants from the optical decay curves, as described herein. In someembodiments, data processing system 1900 is represented by any one ofsignal processing units 790, 890, 990 and 1090 depicted in FIGS. 7, 8,9, and 10, respectively, and further optionally incorporates any one ofdata storage units 792 and 892 depicted in FIGS. 7 and 8, respectively.

Data processing system 1900 includes a processing unit 1901 that mayinclude a microprocessor or microcontroller, such as Intelmicroprocessor (e.g., Core i7, Core 2 Duo, Core 2 Quad, Atom), SunMicrosystems microprocessor (e.g., SPARC), IBM microprocessor (e.g., IBM750), Motorola microprocessor (e.g., Motorola 68000), Advanced MicroDevices (“AMD”) microprocessor, Texas Instrument microcontroller, andany other microprocessor or microcontroller.

Processing unit 1901 may include a personal computer (PC), such as aMacintosh® (from Apple Inc. of Cupertino, Calif.), Windows®-based PC(from Microsoft Corporation of Redmond, Wash.), or one of a wide varietyof hardware platforms that run the UNIX operating system or otheroperating systems. For at least some embodiments, processing unit 1901includes a general purpose or specific purpose data processing systembased on Intel, AMD, Motorola, IBM, Sun Microsystems, IBM processorfamilies, or any other processor families. As shown in FIG. 19, a memory1903 is coupled to the processing unit 1901 by a bus 1923. Memory 1903has instructions and data 1904 stored thereon which when accessed byprocessing unit 1901 cause the processing unit 1901 to perform methodsto provide highly multiplexed particle analysis or lifetime analysis,and optionally sorting, as described herein.

Memory 1903 can be dynamic random access memory (“DRAM”) and can alsoinclude static random access memory (“SRAM”). A bus 1923 couplesprocessing unit 1901 to memory 1903 and also to a non-volatile storage1909 and to a display controller 1905 (if a display is used) and toinput/output (I/O) controller(s) 1911. Display controller 1905 controlsin the conventional manner a display on a display device 1907 which canbe a cathode ray tube (CRT), a liquid crystal display (LCD), alight-emitting diode (LED) monitor, a plasma monitor, or any otherdisplay device. Input/output devices 1917 can include a keyboard, diskdrives, printers, a scanner, a camera, and other input and outputdevices, including a mouse or other pointing device. I/O controller 1911is coupled to one or more audio input devices 1913 such as, for example,one or more microphones.

Display controller 1905 and I/O controller 1911 can be implemented withconventional well-known technology. An audio output 1915 such as, forexample, one or more speakers, may be coupled to I/O controller 1911.Non-volatile storage 1909 is often a magnetic hard disk, an opticaldisk, or another form of storage for large amounts of data. Some of thisdata is often written, by a direct memory access process, into memory1903 during execution of software in data processing system 1900 toperform methods described herein.

One skilled in the art will immediately recognize that the terms“computer-readable medium” and “machine-readable medium” include anytype of storage device that is accessible by processing unit 1901. Dataprocessing system 1900 can interface to external systems through a modemor network interface 1921. It will be appreciated that modem or networkinterface 1921 can be considered to be part of data processing system1900. This interface 1921 can be an analog modem, ISDN modem, cablemodem, token ring interface, satellite transmission interface, Wi-Fi,Bluetooth, cellular network communication interface, or other interfacesfor coupling a data processing system to other data processing systems.

It will be appreciated that data processing system 1900 is one exampleof many possible data processing systems which have differentarchitectures. For example, personal computers based on an Intelmicroprocessor often have multiple buses, one of which can be aninput/output (I/O) bus for the peripherals and one that directlyconnects processing unit 1901 and memory 1903 (often referred to as amemory bus). The buses are connected together through bridge componentsthat perform any appropriate translation due to differing bus protocols.

Network computers are another type of data processing system that can beused with the embodiments as described herein. Network computers do notusually include a hard disk or other mass storage, and the executableprograms are loaded from a network connection into memory 1903 forexecution by processing unit 1901. A typical data processing system willusually include at least a processor, memory, and a bus coupling thememory to the processor.

It will also be appreciated that data processing system 1900 can becontrolled by operating system software which includes a file managementsystem, such as a disk operating system, which is part of the operatingsystem software. Operating system software can be the family ofoperating systems known as Macintosh® Operating System (Mac OS®) or MacOS X® from Apple Inc. of Cupertino, Calif., or the family of operatingsystems known as Windows® from Microsoft Corporation of Redmond, Wash.,and their associated file management systems. The file management systemis typically stored in non-volatile storage 1909 and causes processingunit 1901 to execute the various acts required by the operating systemto input and output data and to store data in memory, including storingfiles on non-volatile storage 1909.

In various embodiments, hardwired circuitry may be used in combinationwith software instructions to implement methods described herein. Anon-transitory machine-readable medium can be used to store software anddata which when executed by a data processing system causes the systemto perform various methods described herein. This executable softwareand data may be stored in various places including for example ROM,volatile RAM, non-volatile memory, and/or cache. Portions of thissoftware and/or data may be stored in any one of these storage devices.

Thus, a machine-readable medium includes any mechanism that provides(i.e., stores and/or transmits) information in a form accessible by amachine (e.g., a computer, network device, or any device with a set ofone or more processors, etc.). For example, a machine-readable mediumincludes recordable/non-recordable media (e.g., read only memory (ROM);random access memory (RAM); magnetic disk storage media; optical storagemedia; flash memory devices; and the like).

It will be further appreciated that data processing system 1900 may befunctionally implemented by allocating several of its functions todistributed units or modules separate from a central system. In someembodiments, some or all of the signal processing functions as depicted,e.g., in FIGS. 7-10, illustrated in FIGS. 11A-F and 12A-B, and describedin FIGS. 18A-C, may be performed by signal processing units or modulesphysically separate from data processing system 1900, yet connected withit for performance of other functions, such as, e.g., input/output,display, data storage, memory usage, bus usage, additional signalprocessing functions, and both specific-purpose and general-purpose dataprocessing functions. In some embodiments, some or all of the datastorage functions as depicted, e.g., in FIGS. 7-8, illustrated in FIGS.11A-F and 12A-B, and described in FIGS. 18A-C, may be performed by datastorage units or modules physically separate from data processing system1900, yet connected with it as described above. In some embodiments,some or all of the signal processing functions mentioned may beperformed by processing unit 1901 internal to data processing system1900, and in some embodiments some or all of the data storage functionsmentioned may be performed by non-volatile storage unit 1909 and/ormemory unit 1903 internal to data processing system 1900.

The methods as described herein can be implemented using dedicatedhardware (e.g., using Field Programmable Gate Arrays, Digital SignalProcessing chips, or Application Specific Integrated Circuits) or sharedcircuitry (e.g., microprocessors, microcontrollers, single-boardcomputers, standalone computers, or cloud-based processors on remoteservers) under control of program instructions stored in amachine-readable medium. The methods as described herein can also beimplemented as computer instructions for execution on a data processingsystem, such as system 1900 of FIG. 19.

It will be appreciated by those skilled in the art that aspects of thepresent disclosure, while illustrated with reference to applications toparticle analysis and sorting and particularly to flow cytometry, alsopresent advantages in other application areas. The concept of lifetimebinning as a means to simplify the performance of lifetime measurementsand thereby enable higher degree of multiplexing than hithertopractical, for example, is also advantageous to the field of imaging, inparticular to the field of microscopy, and more particularly to thefield of fluorescence microscopy. Whereas in a flow cytometer an “event”is defined as the passage of a particle through the interrogation area,in microscopy the roughly equivalent element is a “pixel,” defined asthe smallest resolvable unit of an image. Spectral spillover andcrosstalk is a problem in fluorescence microscopy just as it is aproblem in flow cytometry, and the present disclosure offers a solutionto both by providing a greater degree of multiplexing, a reduced levelof spectral spillover, or a combination of the two. The presentdisclosure admits of implementation within the framework of afluorescence microscope in ways that parallel very closely the specificexamples given in the case of flow-based particle analysis. A microscopyapplication of the present disclosure, for example, would rely on asystem configuration very similar to that of FIG. 7, with the fluidelements 740, 742, 700, and 740 replaced by a suitable sample holder,such as are well known in the art, for exposure of the sample to thebeam; and similarly for FIGS. 9 and 10. In other words, adaptation ofthe present disclosure to the field of microscopy is fully within thescope of this disclosure, given the descriptions of the novel apparatusand methods herein. One specific application of fluorescence microscopythat would benefit from the present disclosure is in vivo imaging, suchas, e.g., methods and apparatuses used for medical diagnostics. Theseinclude the analysis and diagnosis of externally optically accessibleorgans, such as the skin and the eye, as well as organs opticallyaccessible through the use of endoscopes, such as the respiratory tract,the gastrointestinal tract, and, in the context of surgery, any otherorgan or part of the body. As in the case of laboratory-basedfluorescence microscopy, adaptation of the apparatuses and methodsdescribed herein is entirely within the scope of the present disclosure,requiring only minor modifications of the apparatus and process stepsfrom the illustrative examples that are provided. The usefulness of thepresent disclosure is therefore not to be circumscribed to the examplesand figures provided, but extends to the full scope of what is claimed.

It will be further appreciated by those skilled in the art that theconcept of lifetime binning as a means to enable higher degree ofmultiplexing than hitherto possible or practical, for example, is alsoadvantageous to the field of bead-based multiplexing assays. The variousmethods and systems described herein can be applied to the task ofbead-based multiplexing with only minor modifications, such asaccounting for the predesigned levels of each color-coding dye presentin a bead in order to identify which combination of dye level(s) aparticular bead belongs to. In an exemplary embodiment, microspheres(of, e.g., polystyrene or other materials) are formed so as toincorporate a dye having a relatively short fluorescence lifetime (e.g.,dye A, having a lifetime shorter than, e.g., about 5 ns) and another dyehaving a relatively long fluorescence lifetime (e.g., dye B, having alifetime longer than, e.g., about 10 ns), both such dyes havingsubstantially overlapping spectra so as to permit their detection by thesame optical apparatus. Each microsphere is prepared with one of a setof discrete amounts of each dye, e.g., an amount substantially equal to1000, 1500, 2250, 3375, 5063, 7594, 11391, 17086, 25629, or 38443molecules. In certain embodiments, more or less than the ten discreteamounts described in this exemplary embodiment may be preferable. Incertain embodiments, the ratio of one amount to the immediately lowerone may be preferably more or less than the ratio of 1.5 described inthis exemplary embodiment. In certain embodiments, the ratios betweenamounts may preferably be other than the uniform ratio described in thisexemplary embodiment. In certain embodiments, the lowest amount maypreferably be more or less than the 1000 molecules described in thisexemplary embodiment. The various combinations of the two dyes indifferent amounts comprise a multiplexing set; in this exemplaryembodiment, the use of ten different amounts for each of two dyes yields100 different combinations, each combination corresponding to adifferent type of coded microsphere. Each type of coded microsphere inturn is used for a different capture assay: e.g., beads having thecombination of 1000 molecules of dye A and 1000 molecules of dye B areprepared with, e.g., capture antibodies specific to antigen X; beadshaving the combination of 1500 molecules of dye A and 1000 molecules ofdye B are prepared with, e.g., capture antibodies specific to antigen Y;and so forth. The beads so prepared are then used in, e.g., multiplexingsandwich immunoassays, multiplexing nucleic-acid assays, or othermultiplexing assays as described herein and according to methods wellknown to those skilled in the art.

An exemplary embodiment of an instrument used for bead-basedmultiplexing assays according to the present disclosure comprises: apulsed light source designed to simultaneously excite dye A and dye B ineach bead in a sample of beads suspended in buffer medium as the beadflows in single file through a flowcell; an optical apparatus designedto collect fluorescence emission decays from dye A and dye B; and asignal processing apparatus designed to extract the contributions fromdye A and dye B to the observed decay signals; as well as a light sourcedesigned to excite one or more type of fluorescent reporter molecules;an optical apparatus designed to collect fluorescence emission from thereporter molecule(s) present on each detected bead; and a signalprocessing apparatus designed to measure the reporter fluorescenceemission or emissions from each detected bead. The apparatus in thisexemplary embodiment uses the detected levels of fluorescence in thebead-coding channel(s) to identify the specific dye combination that adetected bead belongs to, and therefore to determine what correspondingassay was performed on that bead. By allowing the simultaneousidentification of each possible combination of dye amounts, and byincorporating fluorescence lifetime as an additional parameter fordiscrimination of bead-coding dyes, the apparatus and method of thepresent disclosure allows the multiplexing of assays in greater numbersthan hitherto possible or practical. In certain embodiments, more thantwo dyes having overlapping spectra and having distinct lifetimes areused to code microspheres. In certain embodiments, more than onespectral channel of detection is allocated for the purpose of codingmicrospheres, each channel allowing the use of one or more dyes withoverlapping spectra and distinct lifetimes. In certain embodiments, morethan one light source is allocated for the purpose of exciting dyes usedto code microspheres, and more than one optical apparatus is used tocollect the light emitted by microsphere-coding dyes.

A method of analyzing particles in a sample using a particle analyzer isdisclosed, comprising the steps of:

-   -   providing a source of a beam of pulsed optical energy;    -   providing a sample holder configured to expose a sample to the        beam;    -   providing a detector, the detector comprising a number of        spectral detection channels, the channels being sensitive to        distinct wavelength sections of the electromagnetic spectrum and        being configured to detect optical signals resulting from        interactions between the beam and the sample, the channels being        further configured to convert the optical signals into        respective electrical signals;    -   providing a first optical path from the source of the beam to        the sample;    -   providing a second optical path from the sample to the detector;    -   providing a signal processing module;    -   exposing the sample to the beam, and    -   using the signal processing module for:    -   receiving the electrical signals from the detector;    -   mathematically combining individual decay curves in the        electrical signals into a decay supercurve, said supercurve        comprising a number of components, each component having a time        constant and a relative contribution to the supercurve;    -   extracting time constants from the supercurve; and    -   quantifying the relative contribution of individual components        to the supercurve.

A method of analyzing and sorting particles in a sample using a particleanalyzer/sorter is disclosed, comprising the steps of:

-   -   providing an internally modulated laser as a source of a beam of        pulsed optical energy;    -   providing a flowcell configured as an optical excitation chamber        for exposing to the beam a sample comprising a suspension of        particles and for generating optical signals from interactions        between the beam and the particles;    -   providing a detector, the detector comprising a number of        spectral detection channels, the channels being sensitive to        distinct wavelength sections of the electromagnetic spectrum and        being configured to detect fluorescence optical signals        resulting from interactions between the beam and the particles        in the sample, the channels being further configured to convert        the optical signals into respective electrical signals;    -   providing a first optical path from the source of the beam to        the sample;    -   providing a second optical path from the sample to the detector;    -   providing a flow path for the suspension of particles;    -   providing connections between the flowcell and each of the flow        path, the first optical path, and the second optical path;    -   providing a signal processing module comprising one of an FPGA,        a DSP chip, an ASIC, a CPU, a microprocessor, a microcontroller,        a single-board computer, a standalone computer, and a        cloud-based processor;    -   exposing the particles in the sample to the beam;    -   using the signal processing module for:    -   receiving the electrical signals from the detector;    -   mathematically combining individual decay curves in the        electrical signals into a decay supercurve, said supercurve        comprising a number of components, each component having a time        constant and a relative contribution to the supercurve;    -   extracting time constants from the supercurve; and    -   quantifying the relative contribution of individual components        to the supercurve;    -   providing a particle sorting actuator connected with the flow        path, based on at least one flow diversion in the flow path, and        further based on one of a transient bubble, a pressurizable        chamber, a pressurizable/depressurizable chamber pair, and a        pressure transducer;    -   providing an actuator driver connected with the actuator, the        driver being configured to receive actuation signals from the        signal processing module;    -   providing at least one particle collection receptacle; and    -   collecting at least one particle from the suspension of        particles in the particle collection receptacle.

A method of analyzing particles in a sample using a particle analyzer isdisclosed, comprising the steps of:

-   -   providing a source of a beam of pulsed optical energy;    -   providing a sample holder configured to expose a sample to the        beam;    -   providing a detector, the detector comprising a number of        spectral detection channels, the channels being sensitive to        distinct wavelength sections of the electromagnetic spectrum and        being configured to detect optical signals resulting from        interactions between the beam and the sample, the channels being        further configured to convert the optical signals into        respective electrical signals;    -   providing a first optical path from the source of the beam to        the sample;    -   providing a second optical path from the sample to the detector;    -   providing a signal processing module;    -   exposing the sample to the beam, and    -   using the signal processing module for:    -   receiving the electrical signals from the detector;    -   mathematically combining individual decay curves in the        electrical signals into a decay supercurve, said supercurve        comprising a number of components, each component having a time        constant and a relative contribution to the supercurve;    -   allocating individual components of the supercurve to discrete        bins of predetermined time constants; and    -   quantifying the relative contribution of individual components        to the supercurve.

A method of analyzing and sorting particles in a sample using a particleanalyzer/sorter is disclosed, comprising the steps of:

-   -   providing an internally modulated laser as a source of a beam of        pulsed optical energy;    -   providing a flowcell configured as an optical excitation chamber        for exposing to the beam a sample comprising a suspension of        particles and for generating optical signals from interactions        between the beam and the particles;    -   providing a detector, the detector comprising a number of        spectral detection channels, the channels being sensitive to        distinct wavelength sections of the electromagnetic spectrum and        being configured to detect fluorescence optical signals        resulting from interactions between the beam and the particles        in the sample, the channels being further configured to convert        the optical signals into respective electrical signals;    -   providing a first optical path from the source of the beam to        the sample;    -   providing a second optical path from the sample to the detector;    -   providing a flow path for the suspension of particles;    -   providing connections between the flowcell and each of the flow        path, the first optical path, and the second optical path;    -   providing a signal processing module comprising one of an FPGA,        a DSP chip, an ASIC, a CPU, a microprocessor, a microcontroller,        a single-board computer, a standalone computer, and a        cloud-based processor;    -   exposing the particles in the sample to the beam;    -   using the signal processing module for:    -   receiving the electrical signals from the detector;    -   mathematically combining individual decay curves in the        electrical signals into a decay supercurve, said supercurve        comprising a number of components, each component having a time        constant and a relative contribution to the supercurve;    -   allocating individual components of the supercurve to discrete        bins of predetermined time constants; and    -   quantifying the relative contribution of individual components        to the supercurve;    -   providing a particle sorting actuator connected with the flow        path, based on at least one flow diversion in the flow path, and        further based on one of a transient bubble, a pressurizable        chamber, a pressurizable/depressurizable chamber pair, and a        pressure transducer;    -   providing an actuator driver connected with the actuator, the        driver being configured to receive actuation signals from the        signal processing module;    -   providing at least one particle collection receptacle; and    -   collecting at least one particle from the suspension of        particles in the particle collection receptacle.

In the foregoing specification, embodiments of the current disclosurehave been described with reference to specific exemplary embodimentsthereof. It will, however, be evident that various modifications andchanges may be made thereto without departing from the broader spiritand scope of the disclosure. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense.

1. An apparatus for analyzing an optical signal decay, comprising: asource of a beam of pulsed optical energy; a sample holder configured toexpose a sample to the beam; a detector comprising a number of spectraldetection channels, the channels being sensitive to distinct wavelengthsections of the electromagnetic spectrum and being configured to detectoptical signals resulting from interactions between the beam and thesample, the channels being further configured to convert the opticalsignals into respective electrical signals; a first optical path fromthe source of the beam to the sample; a second optical path from thesample to the detector; and a signal processing module configured toperform a method comprising: receiving the electrical signals from thedetector; mathematically combining individual decay curves in theelectrical signals into a decay supercurve, the supercurve comprising anumber of components, each component having a time constant and arelative contribution to the supercurve; and numerically fitting a modelto the supercurve.
 2. The apparatus of claim 1, wherein numericallyfitting comprises using at least one of: linear regression, nonlinearregression, least squares fitting, nonlinear least squares fitting,partial least squares fitting, weighted fitting, constrained fitting,Levenberg-Marquardt algorithm, Bayesian analysis, principal componentanalysis, cluster analysis, support vector machines, neural networks,machine learning, deep learning, and combinations thereof.
 3. Theapparatus of claim 1, wherein each time constant and each relativecontribution each correspond to at least one adjustable parameter of themodel, wherein the model comprises a plurality of parameters, andwherein one or more of the plurality of parameters is adjusted byfitting.
 4. The apparatus of claim 1, wherein the source of the beam ofpulsed optical energy is an internally modulated laser.
 5. The apparatusof claim 1, wherein the signal processing module comprises one of: anFPGA, a DSP chip, an ASIC, a CPU, a microprocessor, a microcontroller, asingle-board computer, a standalone computer, and a cloud-basedprocessor.
 6. The apparatus of claim 1, wherein the optical signalscomprise a fluorescence signal.
 7. The apparatus of claim 1, wherein thesample comprises a suspension of particles; the apparatus furthercomprising: a flow path for the suspension of particles; and a flowcellconfigured as an optical excitation chamber for generating the opticalsignals from interactions between the beam of pulsed optical energy andthe particles, wherein flowcell is connected with the flow path, thefirst optical path, and the second optical path.
 8. The apparatus ofclaim 7, wherein the apparatus comprises a flow cytometer.
 9. Theapparatus of claim 8, further comprising: a particle sorting actuatorconnected with the flow path; an actuator driver connected with theactuator, the driver configured to receive actuation signals from thesignal processing module; and at least one particle collectionreceptacle connected with the flow path.
 10. The apparatus of claim 9,wherein the particle sorting actuator is based on at least one flowdiversion in the flow path.
 11. The apparatus of claim 10, wherein theparticle sorting actuator is based on one of: a transient bubble, apressurizable chamber, a pressurizable/depressurizable chamber pair, anda pressure transducer.
 12. An apparatus for analyzing an optical signaldecay, comprising: a source of a beam of pulsed optical energy; a sampleholder configured to expose a sample to the beam; a detector comprisinga number of spectral detection channels, the channels being sensitive todistinct wavelength sections of the electromagnetic spectrum and beingconfigured to detect optical signals resulting from interactions betweenthe beam and the sample, the channels being further configured toconvert the optical signals into respective electrical signals; a firstoptical path from the source of the beam to the sample; a second opticalpath from the sample to the detector; and a signal processing moduleconfigured to perform a method comprising: receiving the electricalsignals from the detector; mathematically combining individual decaycurves in the electrical signals into a decay supercurve, the supercurvecomprising a number of components, each component having a time constantand a relative contribution to the supercurve; allocating individualcomponents of the supercurve to discrete bins of predetermined timeconstants; and numerically fitting a model to the supercurve.
 13. Theapparatus of claim 12, wherein numerically fitting comprises using atleast one of: linear regression, nonlinear regression, least squaresfitting, nonlinear least squares fitting, partial least squares fitting,weighted fitting, constrained fitting, Levenberg-Marquardt algorithm,Bayesian analysis, principal component analysis, cluster analysis,support vector machines, neural networks, machine learning, deeplearning, and combinations thereof.
 14. The apparatus of claim 12,wherein each relative contribution corresponds to at least oneadjustable parameter of the model, wherein the model comprises aplurality of parameters, and wherein one or more of the plurality ofparameters is adjusted by fitting.
 15. The apparatus of claim 12,wherein the source of the beam of pulsed optical energy is an internallymodulated laser.
 16. The apparatus of claim 12, wherein the signalprocessing module comprises one of: an FPGA, a DSP chip, an ASIC, a CPU,a microprocessor, a microcontroller, a single-board computer, astandalone computer, and a cloud-based processor.
 17. The apparatus ofclaim 12, wherein the optical signals comprise a fluorescence signal.18. The apparatus of claim 12, wherein the sample comprises a suspensionof particles; the apparatus further comprising: a flow path for thesuspension of particles; and a flowcell configured as an opticalexcitation chamber for generating the optical signals from interactionsbetween the beam of pulsed optical energy and the particles, wherein theflowcell is connected with the flow path, the first optical path, andthe second optical path.
 19. The apparatus of claim 18, wherein theapparatus comprises a flow cytometer.
 20. The apparatus of claim 19,further comprising: a particle sorting actuator connected with the flowpath; an actuator driver connected with the actuator, the driverconfigured to receive actuation signals from the signal processingmodule; and at least one particle collection receptacle connected withthe flow path.
 21. The apparatus of claim 20, wherein the particlesorting actuator is based on at least one flow diversion in the flowpath.
 22. The apparatus of claim 21, wherein the particle sortingactuator is based on one of a transient bubble, a pressurizable chamber,a pressurizable/depressurizable chamber pair, and a pressure transducer.